From cbe166a1ce3e3fbba050cd7836695fb6eb413af5 Mon Sep 17 00:00:00 2001 From: Steffi LaZerte Date: Wed, 27 May 2026 15:10:22 -0500 Subject: [PATCH 01/31] Use testthat edition 3 --- DESCRIPTION | 3 ++- tests/testthat.R | 8 ++++++++ tests/testthat/test-back-compatibility.R | 1 - tests/testthat/test-estimate_R.R | 4 ---- tests/testthat/test-weekly.R | 1 - 5 files changed, 10 insertions(+), 7 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index a6368f98..ff9754a9 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -42,7 +42,7 @@ Imports: patchwork (>= 1.2.0), rlang Suggests: - testthat, + testthat (>= 3.0.0), utils, vdiffr, covr, @@ -61,3 +61,4 @@ Roxygen: list(markdown = TRUE) RoxygenNote: 7.3.3 VignetteBuilder: knitr Remotes: reconverse/incidence2/pkg +Config/testthat/edition: 3 diff --git a/tests/testthat.R b/tests/testthat.R index 70fa4fe7..83eca870 100644 --- a/tests/testthat.R +++ b/tests/testthat.R @@ -1,3 +1,11 @@ +# This file is part of the standard setup for testthat. +# It is recommended that you do not modify it. +# +# Where should you do additional test configuration? +# Learn more about the roles of various files in: +# * https://r-pkgs.org/testing-design.html#sec-tests-files-overview +# * https://testthat.r-lib.org/articles/special-files.html + library(testthat) library(EpiEstim) diff --git a/tests/testthat/test-back-compatibility.R b/tests/testthat/test-back-compatibility.R index b91c91cd..c4f98543 100644 --- a/tests/testthat/test-back-compatibility.R +++ b/tests/testthat/test-back-compatibility.R @@ -1,5 +1,4 @@ test_that("These things still calculate the same after refactoring", { - local_edition(3) data("Flu2009") x1 <- estimate_R( diff --git a/tests/testthat/test-estimate_R.R b/tests/testthat/test-estimate_R.R index 032a6fb7..8ca71faf 100644 --- a/tests/testthat/test-estimate_R.R +++ b/tests/testthat/test-estimate_R.R @@ -13,7 +13,6 @@ test_that("There is an informative error if dates are not ascending", { # The following examples are from EpiEstim's documentation. test_that("Example 1 matches saved output", { - testthat::local_edition(3) out <- estimate_R(Flu2009$incidence, method = "non_parametric_si", config = list(t_start = 2:26, t_end = 8:32, si_distr = Flu2009$si_distr, @@ -22,7 +21,6 @@ test_that("Example 1 matches saved output", { }) test_that("Example 2 matches saved output", { - testthat::local_edition(3) data <- c(0, 1, 1, 2, 1, 3, 4, 5, 5, 5, 5, 4, 4, 26, 6, 7, 9) location <- c("imported", "local", "imported", "imported", "local", "imported", "imported", "imported", "imported", @@ -39,7 +37,6 @@ test_that("Example 2 matches saved output", { }) test_that("Example 3 matches saved output", { - testthat::local_edition(3) out <- estimate_R(Flu2009$incidence, method = "parametric_si", config = list(t_start = 2:26, t_end = 8:32, @@ -50,7 +47,6 @@ test_that("Example 3 matches saved output", { }) test_that("Example 4 matches saved output", { - testthat::local_edition(3) out <- estimate_R(Flu2009$incidence, method = "uncertain_si", config = list(t_start = 2:26, t_end = 8:32, diff --git a/tests/testthat/test-weekly.R b/tests/testthat/test-weekly.R index 9637c408..b26004cc 100644 --- a/tests/testthat/test-weekly.R +++ b/tests/testthat/test-weekly.R @@ -607,7 +607,6 @@ test_that("grid in estimate_R_agg is in the correct format", { test_that("you can't run estimate_R_agg unless using parametric/non-parametric SI methods", { - testthat::local_edition(3) method <- "uncertain_si" config <- make_config(list(mean_si = 2.6, std_mean_si = 1, min_mean_si = 1, max_mean_si = 4.2, From 9787acbe0ab327bed31858f81a3d3e8ab0c9ce00 Mon Sep 17 00:00:00 2001 From: Steffi LaZerte Date: Wed, 27 May 2026 15:27:52 -0500 Subject: [PATCH 02/31] Add mini samplers snapshots --- R/test_utils.R | 28 + tests/testthat/_snaps/samplers.md | 21225 ++++++++++++++++++++++++++++ tests/testthat/test-samplers.R | 41 + 3 files changed, 21294 insertions(+) create mode 100644 tests/testthat/_snaps/samplers.md diff --git a/R/test_utils.R b/R/test_utils.R index 8d9a0d30..cba48f97 100644 --- a/R/test_utils.R +++ b/R/test_utils.R @@ -35,3 +35,31 @@ epi_expect_doppelganger <- function(name, code, variant) { ) } +#' Custom snapshot utility function +#' +#' Creates mini snapshots by sampling `x` down to `n`. If `x` is a list, the +#' sampling applies to all sub-items (which may or may not be appropriate, +#' depending on the situation). Also rounds to 8 digits to avoid mismatches in +#' re-loading values with long trailing decimals. +#' +#' @param x Vector or list of vectors to take a snapshot of. +#' @param style Snapshot style (see `?testthat::snapshot_value()`) +#' @param n Numeric. Number of samples to keep +#' +#' @noRd +epi_snapshot_value <- function(x, style = "json2", n = 5) { + set.seed(1) # TODO: could use withr::with_seed() + + sample_x <- function(xx) { + # take random sample of 5 points or all + nn <- if(length(xx) < n) length(xx) else n + xx <- sample(xx, size = nn) + # Round to avoid mismatches from truncated records + if(is.numeric(xx)) xx <- round(xx, digits = 8) + xx + } + + if(is.list(x)) x <- lapply(x, sample_x) else x <- sample_x(x) + + testthat::expect_snapshot_value(x, style = style) +} diff --git a/tests/testthat/_snaps/samplers.md b/tests/testthat/_snaps/samplers.md new file mode 100644 index 00000000..fa426b1d --- /dev/null +++ b/tests/testthat/_snaps/samplers.md @@ -0,0 +1,21225 @@ +# draw_epsilon produces expected results (2 variants, 4 locations) + + { + "type": "double", + "attributes": {}, + "value": [1.00245545, 0.99460201, 1.01794685, 1.02167213, 1.00642198] + } + +# draw_epsilon produces expected results (2 variants, 1 location) + + { + "type": "double", + "attributes": {}, + "value": [0.9934614, 0.97786211, 1.02441227, 1.03189037, 1.00136363] + } + +# draw_epsilon produces expected results (>2 variants, 4 locations) + + { + "type": "double", + "attributes": {}, + "value": [1.04495952, 1.01610473, 0.99460201, 1.01794685, 1.02167213] + } + +# draw_epsilon produces expected results (>2 variants, 1 location) + + { + "type": "double", + "attributes": {}, + "value": [1.07894037, 1.02071943, 0.97786211, 1.02441227, 1.03189037] + } + +# draw_epsilon seed + + { + "type": "double", + "attributes": {}, + "value": [0.99069615, 1.05387124, 0.99069615, 0.99069615, 1.05387124] + } + +# draw_R produces expected results (2 variants, 4 locations) + + { + "type": "list", + "attributes": {}, + "value": [ + { + "type": "double", + "attributes": {}, + "value": [0.89091945, 1.34883043, 0.84282641, 0.63244426, 0.79784422] + }, + { + "type": "double", + "attributes": {}, + "value": [1.18978029, 0.72531849, 0.94513563, 0.8134282, 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0.91872568, 0.92931325, 1.0098313] + }, + { + "type": "double", + "attributes": {}, + "value": [1.1840901, 0.94950742, 0.90829443, 0.82163425, 0.92187974] + }, + { + "type": "double", + "attributes": {}, + "value": [1.30985435, 1.10119917, 1.22479941, 1.12209552, 1.03409886] + }, + { + "type": "double", + "attributes": {}, + "value": [1.02371721, 1.43504051, 0.70711612, 0.91517736, 0.92705237] + }, + { + "type": "double", + "attributes": {}, + "value": [1.08351932, 0.97703318, 1.08956516, 5.86414131, 0.66429686] + }, + { + "type": "double", + "attributes": {}, + "value": [0.99384146, 1.00789185, 1.14558437, 0.81560719, 1.15170495] + }, + { + "type": "double", + "attributes": {}, + "value": [1.09923865, 1.43952633, 0.84439554, 1.09476968, 1.04893379] + }, + { + "type": "double", + "attributes": {}, + "value": [1.16973054, 0.9341094, 1.029199, 1.27321455, 1.03215103] + }, + { + "type": "double", + "attributes": {}, + "value": [1.43761901, 1.12434513, 1.05210802, 0.8258976, 1.12293954] + }, + { + "type": "double", + "attributes": {}, + "value": [1.03014132, 1.23144254, 0.96385884, 1.09282326, 1.02036047] + }, + { + "type": "double", + "attributes": {}, + "value": [0.91105847, 1.3539208, 0.91266624, 1.10809012, 1.15436935] + }, + { + "type": "double", + "attributes": {}, + "value": [0.84081234, 1.05875052, 0.81476831, 0.65777622, 0.76078093] + }, + { + "type": "double", + "attributes": {}, + "value": [0.82726771, 0.61826383, 0.94816643, 0.857361, 0.98691717] + }, + { + "type": "double", + "attributes": {}, + "value": [0.98399073, 0.87407845, 1.04866385, 0.93402147, 0.89719924] + }, + { + "type": "double", + "attributes": {}, + "value": [0.71160521, 1.06269352, 1.03769216, 0.92901944, 0.96462351] + }, + { + "type": "double", + "attributes": {}, + "value": [0.83090443, 1.47055187, 1.90171408, 1.06130498, 0.81102346] + }, + { + "type": "double", + "attributes": {}, + "value": [1.03128215, 0.77622841, 0.82438572, 1.14246141, 1.08937777] + }, + { + "type": "double", + "attributes": {}, + "value": [1.12071756, 1.10968423, 1.65652228, 0.88839436, 0.6252615] + }, + { + "type": "double", + "attributes": {}, + "value": [1.19883464, 0.98189805, 0.96405516, 1.34610617, 1.22202253] + }, + { + "type": "double", + "attributes": {}, + "value": [0.89060588, 1.21306392, 1.0479908, 0.99898242, 1.0280535] + }, + { + "type": "double", + "attributes": {}, + "value": [0.70131557, 0.81026604, 0.9568454, 0.95702748, 1.0753382] + }, + { + "type": "double", + "attributes": {}, + "value": [0.90915351, 1.39048681, 0.95800224, 0.96561168, 0.75170525] + }, + { + "type": "double", + "attributes": {}, + "value": [0.9362269, 4.93545726, 0.84146534, 0.99160021, 0.96476401] + }, + { + "type": "double", + "attributes": {}, + "value": [1.15614555, 1.15949524, 0.92058974, 1.17265438, 0.57857352] + }, + { + "type": "double", + "attributes": {}, + "value": [0.83229567, 0.99413351, 1.2232836, 0.87871553, 1.5867565] + }, + { + "type": "double", + "attributes": {}, + "value": [1.04362067, 1.13609676, 0.82218108, 0.75947048, 1.13943133] + }, + { + "type": "double", + "attributes": {}, + "value": [1.1279818, 0.87093581, 1.04521808, 0.76539312, 1.00739028] + }, + { + "type": "double", + "attributes": {}, + "value": [1.17742798, 0.99182727, 1.74909366, 1.16522569, 0.72059191] + }, + { + "type": "double", + "attributes": {}, + "value": [0.77371031, 1.30829533, 1.31944095, 0.79698483, 1.0860643] + }, + { + "type": "double", + "attributes": {}, + "value": [1.04964521, 1.13035689, 0.96169532, 1.03289603, 1.22506162] + }, + { + "type": "double", + "attributes": {}, + "value": [0.86626055, 1.07036591, 1.06067246, 0.88515662, 1.159652] + }, + { + "type": "double", + "attributes": {}, + "value": [0.92388167, 0.90782827, 1.03317797, "NA", 1.13686186] + }, + { + "type": "double", + "attributes": {}, + "value": [1.00931018, 1.04114407, 0.94987142, 0.79924762, 0.72657478] + }, + { + "type": "double", + "attributes": {}, + "value": [1.07820736, 0.76681264, 0.8879739, 0.88747212, 0.96299102] + }, + { + "type": "double", + "attributes": {}, + "value": [0.86893955, 0.99996307, 0.85886411, 1.04609967, 1.27063522] + }, + { + "type": "double", + "attributes": {}, + "value": [1.10386284, 0.88794437, 0.89142135, 0.77290176, 1.06035483] + }, + { + "type": "double", + "attributes": {}, + "value": [1.2889003, 0.90170836, 0.92954183, 1.06983306, 1.20060373] + }, + { + "type": "double", + "attributes": {}, + "value": ["NA", 0.88155608, 0.7998402, 1.06170236, 1.22675695] + }, + { + "type": "double", + "attributes": {}, + "value": [5.48948942, 0.80210223, 1.003078, 1.05240546, 1.57239736] + }, + { + "type": "double", + "attributes": {}, + "value": [1.29695662, 1.13476602, 0.8686475, 0.76106537, 1.02588636] + }, + { + "type": "double", + "attributes": {}, + "value": [1.03979547, 1.30167111, 1.08783074, 0.91452611, 1.00292209] + }, + { + "type": "double", + "attributes": {}, + "value": [1.18971251, 1.33057677, 1.05800672, 0.98065699, 1.07978156] + }, + { + "type": "double", + "attributes": {}, + "value": [0.88938577, 1.0947158, 1.55234684, 0.79283602, 1.2496024] + }, + { + "type": "double", + "attributes": {}, + "value": [1.1344501, 0.98443236, 1.13375315, 1.20999502, 1.27016243] + }, + { + "type": "double", + "attributes": {}, + "value": [0.78204991, 1.07015584, 0.71224649, 0.94610723, 1.85328681] + } + ] + } + +# draw_R seed + + { + "type": "double", + "attributes": {}, + "value": [0.94404007, 1.12731997, "NA", 0.9538139, 1.32909981] + } + +# estimate_advantage produces expected results (2 variants 3 locations) + + { + "type": "list", + "attributes": { + "names": { + "type": "character", + "attributes": {}, + "value": ["epsilon", "R", "convergence", "diag"] + } + }, + "value": [ + { + "type": "double", + "attributes": {}, + "value": [1.04125274, 1.04420411, 0.98760227, 0.9614608, 0.98635696] + }, + { + "type": "double", + "attributes": {}, + "value": [0.83953868, 0.91588217, 1.06571987, 0.90559891, 0.84006488] + }, + { + "type": "logical", + "attributes": {}, + "value": [true] + }, + { + "type": "list", + "attributes": {}, + "value": [ + { + "type": "list", + "attributes": { + "names": { + "type": "character", + "attributes": {}, + "value": ["psrf", "mpsrf"] + }, + "class": { + "type": "character", + "attributes": {}, + "value": ["gelman.diag"] + } + }, + "value": [ + { + "type": "double", + "attributes": { + "dim": { + "type": "integer", + "attributes": {}, + "value": [1, 2] + }, + "dimnames": { + "type": "list", + "attributes": {}, + "value": [ + { + "type": "NULL" + }, + { + "type": "character", + "attributes": {}, + "value": ["Point est.", "Upper C.I."] + } + ] + } + }, + "value": [0.99906146, 1.0075281] + }, + { + "type": "NULL" + } + ] + } + ] + } + ] + } + +# estimate_advantage produces expected results (2 variants 1 location) + + { + "type": "list", + "attributes": { + "names": { + "type": "character", + "attributes": {}, + "value": ["epsilon", "R", "convergence", "diag"] + } + }, + "value": [ + { + "type": "double", + "attributes": {}, + "value": [1.04271486, 0.95213998, 0.9775455, 1.00751841, 1.06043541] + }, + { + "type": "double", + "attributes": {}, + "value": [1.11224369, 1.0134272, 0.94738761, 0.8710692, "NA"] + }, + { + "type": "logical", + "attributes": {}, + "value": [true] + }, + { + "type": "list", + "attributes": {}, + "value": [ + { + "type": "list", + "attributes": { + "names": { + "type": "character", + "attributes": {}, + "value": ["psrf", "mpsrf"] + }, + "class": { + "type": "character", + "attributes": {}, + "value": ["gelman.diag"] + } + }, + "value": [ + { + "type": "double", + "attributes": { + "dim": { + "type": "integer", + "attributes": {}, + "value": [1, 2] + }, + "dimnames": { + "type": "list", + "attributes": {}, + "value": [ + { + "type": "NULL" + }, + { + "type": "character", + "attributes": {}, + "value": ["Point est.", "Upper C.I."] + } + ] + } + }, + "value": [0.99676223, 1.04637177] + }, + { + "type": "NULL" + } + ] + } + ] + } + ] + } + +# estimate_advantage produces expected results (>2 variants 4 locs) + + { + "type": "list", + "attributes": { + "names": { + "type": "character", + "attributes": {}, + "value": ["epsilon", "R", "convergence", "diag"] + } + }, + "value": [ + { + "type": "double", + "attributes": {}, + "value": [0.97699048, 0.97927712, 0.96709735, 1.0127237, 0.9741251] + }, + { + "type": "double", + "attributes": {}, + "value": [1.34708698, 1.17052848, 0.92082335, 0.77482061, 1.10078572] + }, + { + "type": "logical", + "attributes": {}, + "value": [true, true] + }, + { + "type": "list", + "attributes": {}, + "value": [ + { + "type": "list", + "attributes": { + "names": { + "type": "character", + "attributes": {}, + "value": ["psrf", "mpsrf"] + }, + "class": { + "type": "character", + "attributes": {}, + "value": ["gelman.diag"] + } + }, + "value": [ + { + "type": "double", + "attributes": { + "dim": { + "type": "integer", + "attributes": {}, + "value": [1, 2] + }, + "dimnames": { + "type": "list", + "attributes": {}, + "value": [ + { + "type": "NULL" + }, + { + "type": "character", + "attributes": {}, + "value": ["Point est.", "Upper C.I."] + } + ] + } + }, + "value": [0.98478887, 0.99198357] + }, + { + "type": "NULL" + } + ] + }, + { + "type": "list", + "attributes": { + "names": { + "type": "character", + "attributes": {}, + "value": ["psrf", "mpsrf"] + }, + "class": { + "type": "character", + "attributes": {}, + "value": ["gelman.diag"] + } + }, + "value": [ + { + "type": "double", + "attributes": { + "dim": { + "type": "integer", + "attributes": {}, + "value": [1, 2] + }, + "dimnames": { + "type": "list", + "attributes": {}, + "value": [ + { + "type": "NULL" + }, + { + "type": "character", + "attributes": {}, + "value": ["Point est.", "Upper C.I."] + } + ] + } + }, + "value": [0.99367021, 1.00567634] + }, + { + "type": "NULL" + } + ] + } + ] + } + ] + } + +# estimate_advantage produces expected results (>2 variants 1 loc) + + { + "type": "list", + "attributes": { + "names": { + "type": "character", + "attributes": {}, + "value": ["epsilon", "R", "convergence", "diag"] + } + }, + "value": [ + { + "type": "double", + "attributes": {}, + "value": [1.00070896, 1.01226561, 1.00211123, 1.03963539, 1.04309282] + }, + { + "type": "double", + "attributes": {}, + "value": [1.0033329, 0.9216911, 1.04504697, 0.88010213, "NA"] + }, + { + "type": "logical", + "attributes": {}, + "value": [false, true] + }, + { + "type": "list", + "attributes": {}, + "value": [ + { + "type": "list", + "attributes": { + "names": { + "type": "character", + "attributes": {}, + "value": ["psrf", "mpsrf"] + }, + "class": { + "type": "character", + "attributes": {}, + "value": ["gelman.diag"] + } + }, + "value": [ + { + "type": "double", + "attributes": { + "dim": { + "type": "integer", + "attributes": {}, + "value": [1, 2] + }, + "dimnames": { + "type": "list", + "attributes": {}, + "value": [ + { + "type": "NULL" + }, + { + "type": "character", + "attributes": {}, + "value": ["Point est.", "Upper C.I."] + } + ] + } + }, + "value": [1.08588305, 1.2273234] + }, + { + "type": "NULL" + } + ] + }, + { + "type": "list", + "attributes": { + "names": { + "type": "character", + "attributes": {}, + "value": ["psrf", "mpsrf"] + }, + "class": { + "type": "character", + "attributes": {}, + "value": ["gelman.diag"] + } + }, + "value": [ + { + "type": "double", + "attributes": { + "dim": { + "type": "integer", + "attributes": {}, + "value": [1, 2] + }, + "dimnames": { + "type": "list", + "attributes": {}, + "value": [ + { + "type": "NULL" + }, + { + "type": "character", + "attributes": {}, + "value": ["Point est.", "Upper C.I."] + } + ] + } + }, + "value": [1.06626227, 1.09796743] + }, + { + "type": "NULL" + } + ] + } + ] + } + ] + } + +# process_I_multivariant works as expected + + { + "type": "list", + "attributes": { + "names": { + "type": "character", + "attributes": {}, + "value": ["local", "imported"] + } + }, + "value": [ + { + "type": "double", + "attributes": {}, + "value": [10, 10, 10, 10, 10] + }, + { + "type": "double", + "attributes": {}, + "value": [0, 0, 0, 0, 0] + } + ] + } + +# estimate_advantage produces expected results (>2var, 1loc, imports) + + { + "type": "list", + "attributes": { + "names": { + "type": "character", + "attributes": {}, + "value": ["epsilon", "R", "convergence", "diag"] + } + }, + "value": [ + { + "type": "double", + "attributes": {}, + "value": [0.00042594, 0.00086427, 0.0002672, 0.00318991, 0.00002992] + }, + { + "type": "double", + "attributes": {}, + "value": [1.12670783, 0.92096564, 0.93871778, 0.92311394, "NA"] + }, + { + "type": "logical", + "attributes": {}, + "value": [false, true] + }, + { + "type": "list", + "attributes": {}, + "value": [ + { + "type": "list", + "attributes": { + "names": { + "type": "character", + "attributes": {}, + "value": ["psrf", "mpsrf"] + }, + "class": { + "type": "character", + "attributes": {}, + "value": ["gelman.diag"] + } + }, + "value": [ + { + "type": "double", + "attributes": { + "dim": { + "type": "integer", + "attributes": {}, + "value": [1, 2] + }, + "dimnames": { + "type": "list", + "attributes": {}, + "value": [ + { + "type": "NULL" + }, + { + "type": "character", + "attributes": {}, + "value": ["Point est.", "Upper C.I."] + } + ] + } + }, + "value": [1.30138256, 2.76090238] + }, + { + "type": "NULL" + } + ] + }, + { + "type": "list", + "attributes": { + "names": { + "type": "character", + "attributes": {}, + "value": ["psrf", "mpsrf"] + }, + "class": { + "type": "character", + "attributes": {}, + "value": ["gelman.diag"] + } + }, + "value": [ + { + "type": "double", + "attributes": { + "dim": { + "type": "integer", + "attributes": {}, + "value": [1, 2] + }, + "dimnames": { + "type": "list", + "attributes": {}, + "value": [ + { + "type": "NULL" + }, + { + "type": "character", + "attributes": {}, + "value": ["Point est.", "Upper C.I."] + } + ] + } + }, + "value": [1.02180146, 1.04177601] + }, + { + "type": "NULL" + } + ] + } + ] + } + ] + } + +# estimate_advantage produces expected results (>2var, 4loc, imports) + + { + "type": "list", + "attributes": { + "names": { + "type": "character", + "attributes": {}, + "value": ["epsilon", "R", "convergence", "diag"] + } + }, + "value": [ + { + "type": "double", + "attributes": {}, + "value": [6.8e-06, 0.00010827, 0.00009129, 0.0000513, 0.00009835] + }, + { + "type": "double", + "attributes": {}, + "value": [0.74513841, 0.93045526, 1.07865269, 0.79847393, 1.99595141] + }, + { + "type": "logical", + "attributes": {}, + "value": [false, true] + }, + { + "type": "list", + "attributes": {}, + "value": [ + { + "type": "list", + "attributes": { + "names": { + "type": "character", + "attributes": {}, + "value": ["psrf", "mpsrf"] + }, + "class": { + "type": "character", + "attributes": {}, + "value": ["gelman.diag"] + } + }, + "value": [ + { + "type": "double", + "attributes": { + "dim": { + "type": "integer", + "attributes": {}, + "value": [1, 2] + }, + "dimnames": { + "type": "list", + "attributes": {}, + "value": [ + { + "type": "NULL" + }, + { + "type": "character", + "attributes": {}, + "value": ["Point est.", "Upper C.I."] + } + ] + } + }, + "value": [1.08057539, 1.25813639] + }, + { + "type": "NULL" + } + ] + }, + { + "type": "list", + "attributes": { + "names": { + "type": "character", + "attributes": {}, + "value": ["psrf", "mpsrf"] + }, + "class": { + "type": "character", + "attributes": {}, + "value": ["gelman.diag"] + } + }, + "value": [ + { + "type": "double", + "attributes": { + "dim": { + "type": "integer", + "attributes": {}, + "value": [1, 2] + }, + "dimnames": { + "type": "list", + "attributes": {}, + "value": [ + { + "type": "NULL" + }, + { + "type": "character", + "attributes": {}, + "value": ["Point est.", "Upper C.I."] + } + ] + } + }, + "value": [0.99512604, 1.00472554] + }, + { + "type": "NULL" + } + ] + } + ] + } + ] + } + +# estimate_advantage produces expected results (2var, 1loc, imports) + + { + "type": "list", + "attributes": { + "names": { + "type": "character", + "attributes": {}, + "value": ["epsilon", "R", "convergence", "diag"] + } + }, + "value": [ + { + "type": "double", + "attributes": {}, + "value": [0.00158209, 0.00021575, 0.0001383, 0.00009621, 0.00039953] + }, + { + "type": "double", + "attributes": {}, + "value": [0.91545587, 1.26842295, 0.65674867, 1.41384318, "NA"] + }, + { + "type": "logical", + "attributes": {}, + "value": [true] + }, + { + "type": "list", + "attributes": {}, + "value": [ + { + "type": "list", + "attributes": { + "names": { + "type": "character", + "attributes": {}, + "value": ["psrf", "mpsrf"] + }, + "class": { + "type": "character", + "attributes": {}, + "value": ["gelman.diag"] + } + }, + "value": [ + { + "type": "double", + "attributes": { + "dim": { + "type": "integer", + "attributes": {}, + "value": [1, 2] + }, + "dimnames": { + "type": "list", + "attributes": {}, + "value": [ + { + "type": "NULL" + }, + { + "type": "character", + "attributes": {}, + "value": ["Point est.", "Upper C.I."] + } + ] + } + }, + "value": [0.99088731, 0.99936699] + }, + { + "type": "NULL" + } + ] + } + ] + } + ] + } + +# estimate_advantage produces expected results (2var, 4loc, imports) + + { + "type": "list", + "attributes": { + "names": { + "type": "character", + "attributes": {}, + "value": ["epsilon", "R", "convergence", "diag"] + } + }, + "value": [ + { + "type": "double", + "attributes": {}, + "value": [0.0003932, 0.00038339, 0.00005313, 0.00002397, 0.00025155] + }, + { + "type": "double", + "attributes": {}, + "value": [1.46667941, 0.92564963, 0.72628082, 0.8210093, 1.39432385] + }, + { + "type": "logical", + "attributes": {}, + "value": [true] + }, + { + "type": "list", + "attributes": {}, + "value": [ + { + "type": "list", + "attributes": { + "names": { + "type": "character", + "attributes": {}, + "value": ["psrf", "mpsrf"] + }, + "class": { + "type": "character", + "attributes": {}, + "value": ["gelman.diag"] + } + }, + "value": [ + { + "type": "double", + "attributes": { + "dim": { + "type": "integer", + "attributes": {}, + "value": [1, 2] + }, + "dimnames": { + "type": "list", + "attributes": {}, + "value": [ + { + "type": "NULL" + }, + { + "type": "character", + "attributes": {}, + "value": ["Point est.", "Upper C.I."] + } + ] + } + }, + "value": [1.03628412, 1.03693989] + }, + { + "type": "NULL" + } + ] + } + ] + } + ] + } + +# estimate_advantage produces expected results (2 var, 2 loc, R_loc1 = 1.1, R_loc2 = 1.5) + + { + "type": "list", + "attributes": { + "names": { + "type": "character", + "attributes": {}, + "value": ["epsilon", "R", "convergence", "diag"] + } + }, + "value": [ + { + "type": "double", + "attributes": {}, + "value": [1.50002569, 1.49999116, 1.50000881, 1.5000003, 1.50000572] + }, + { + "type": "double", + "attributes": {}, + "value": [1.49998719, 1.09996732, 1.09960771, 1.09999027, 1.49998392] + }, + { + "type": "logical", + "attributes": {}, + "value": [false] + }, + { + "type": "list", + "attributes": {}, + "value": [ + { + "type": "list", + "attributes": { + "names": { + "type": "character", + "attributes": {}, + "value": ["psrf", "mpsrf"] + }, + "class": { + "type": "character", + "attributes": {}, + "value": ["gelman.diag"] + } + }, + "value": [ + { + "type": "double", + "attributes": { + "dim": { + "type": "integer", + "attributes": {}, + "value": [1, 2] + }, + "dimnames": { + "type": "list", + "attributes": {}, + "value": [ + { + "type": "NULL" + }, + { + "type": "character", + "attributes": {}, + "value": ["Point est.", "Upper C.I."] + } + ] + } + }, + "value": [1.21752296, 1.7587419] + }, + { + "type": "NULL" + } + ] + } + ] + } + ] + } + +# estimate_advantage uses the correct t_min + + { + "type": "list", + "attributes": { + "names": { + "type": "character", + "attributes": {}, + "value": ["epsilon", "R", "convergence", "diag"] + } + }, + "value": [ + { + "type": "double", + "attributes": {}, + "value": [1.00471817, 1.03107137, 1.02035086, 1.00506093, 1.00732389] + }, + { + "type": "double", + "attributes": {}, + "value": [0.95435841, 1.31424638, 0.64631375, 1.02244546, 1.14068966] + }, + { + "type": "logical", + "attributes": {}, + "value": [false] + }, + { + "type": "list", + "attributes": {}, + "value": [ + { + "type": "list", + "attributes": { + "names": { + "type": "character", + "attributes": {}, + "value": ["psrf", "mpsrf"] + }, + "class": { + "type": "character", + "attributes": {}, + "value": ["gelman.diag"] + } + }, + "value": [ + { + "type": "double", + "attributes": { + "dim": { + "type": "integer", + "attributes": {}, + "value": [1, 2] + }, + "dimnames": { + "type": "list", + "attributes": {}, + "value": [ + { + "type": "NULL" + }, + { + "type": "character", + "attributes": {}, + "value": ["Point est.", "Upper C.I."] + } + ] + } + }, + "value": [1.02097152, 1.11993594] + }, + { + "type": "NULL" + } + ] + } + ] + } + ] + } + +# estimate_advantage convergence checks work with >2 variants + + { + "type": "list", + "attributes": { + "names": { + "type": "character", + "attributes": {}, + "value": ["epsilon", "R", "convergence", "diag"] + } + }, + "value": [ + { + "type": "double", + "attributes": {}, + "value": [0.99403141, 1.0081723, 1.03317077, 1.00100681, 0.97877066] + }, + { + "type": "double", + "attributes": {}, + "value": [1.02441156, 1.44535094, 1.0483692, 0.86256664, 0.77284348] + }, + { + "type": "logical", + "attributes": {}, + "value": [false, true] + }, + { + "type": "list", + "attributes": {}, + "value": [ + { + "type": "list", + "attributes": { + "names": { + "type": "character", + "attributes": {}, + "value": ["psrf", "mpsrf"] + }, + "class": { + "type": "character", + "attributes": {}, + "value": ["gelman.diag"] + } + }, + "value": [ + { + "type": "double", + "attributes": { + "dim": { + "type": "integer", + "attributes": {}, + "value": [1, 2] + }, + "dimnames": { + "type": "list", + "attributes": {}, + "value": [ + { + "type": "NULL" + }, + { + "type": "character", + "attributes": {}, + "value": ["Point est.", "Upper C.I."] + } + ] + } + }, + "value": [1.27871678, 1.98232008] + }, + { + "type": "NULL" + } + ] + }, + { + "type": "list", + "attributes": { + "names": { + "type": "character", + "attributes": {}, + "value": ["psrf", "mpsrf"] + }, + "class": { + "type": "character", + "attributes": {}, + "value": ["gelman.diag"] + } + }, + "value": [ + { + "type": "double", + "attributes": { + "dim": { + "type": "integer", + "attributes": {}, + "value": [1, 2] + }, + "dimnames": { + "type": "list", + "attributes": {}, + "value": [ + { + "type": "NULL" + }, + { + "type": "character", + "attributes": {}, + "value": ["Point est.", "Upper C.I."] + } + ] + } + }, + "value": [1.04497671, 1.07304128] + }, + { + "type": "NULL" + } + ] + } + ] + } + ] + } + diff --git a/tests/testthat/test-samplers.R b/tests/testthat/test-samplers.R index 75227051..101505c9 100644 --- a/tests/testthat/test-samplers.R +++ b/tests/testthat/test-samplers.R @@ -26,6 +26,8 @@ test_that("draw_epsilon produces expected results (2 variants, 4 locations)", { ## epsilon should be approximately 1 ## not exactly 1 because of the first few timesteps & because of priors expect_equal(mean(x), 1, tolerance = 0.05) + + epi_snapshot_value(x) }) @@ -55,6 +57,8 @@ test_that("draw_epsilon produces expected results (2 variants, 1 location)", { ## epsilon should be approximately 1 ## not exactly 1 because of the first few timesteps & because of priors expect_equal(mean(x), 1, tolerance = 0.05) + + epi_snapshot_value(x) }) @@ -86,6 +90,8 @@ test_that("draw_epsilon produces expected results (>2 variants, 4 locations)", { expect_equal( rowMeans(x), c(1, 1), tolerance = 0.05 ) + + epi_snapshot_value(x) }) @@ -115,9 +121,12 @@ test_that("draw_epsilon produces expected results (>2 variants, 1 location)", { ## epsilon should be approximately 1 ## not exactly 1 because of the first few timesteps & because of priors expect_equal(rowMeans(x), c(1, 1), tolerance = 0.05) + + epi_snapshot_value(x) }) + test_that("draw_R produces expected results (2 variants, 4 locations)", { n_v <- 2 # 2 variants n_loc <- 4 # 4 locations @@ -145,6 +154,8 @@ test_that("draw_R produces expected results (2 variants, 4 locations)", { ## not exactly 1 because of the first few timesteps & because of priors ## so ignore fisrt timesteps expect_lt(max(abs(x_mean[-c(1, 2, 3), ] - 1)), 0.05) + + epi_snapshot_value(x) }) @@ -175,6 +186,8 @@ test_that("draw_R produces expected results (2 variants, 1 location)", { ## not exactly 1 because of the first few timesteps & because of priors ## so ignore fisrt timesteps expect_lt(max(abs(x_mean[-c(1, 2, 3), ] - 1)), 0.05) + + epi_snapshot_value(x) }) @@ -205,6 +218,8 @@ test_that("draw_R produces expected results (>2 variants, 4 locations)", { ## not exactly 1 because of the first few timesteps & because of priors ## so ignore fisrt timesteps expect_lt(max(abs(x_mean[-c(1, 2, 3), ] - 1)), 0.05) + + epi_snapshot_value(x) }) @@ -235,6 +250,9 @@ test_that("draw_R produces expected results (>2 variants, 1 location)", { ## not exactly 1 because of the first few timesteps & because of priors ## so ignore fisrt timesteps expect_lt(max(abs(x_mean[-c(1, 2, 3), ] - 1)), 0.05) + + epi_snapshot_value(x) +}) }) @@ -262,6 +280,8 @@ test_that("estimate_advantage produces expected results (2 variants 3 locations) ## so ignore fisrt timesteps mean_R <- rowMeans(x$R, dims = 2) expect_lt(max(abs(mean_R[-c(1, 2, 3), ] - 1)), 0.1) + + epi_snapshot_value(x) }) @@ -289,6 +309,8 @@ test_that("estimate_advantage produces expected results (2 variants 1 location)" ## so ignore fisrt timesteps mean_R <- rowMeans(x$R, dims = 2) expect_lt(max(abs(mean_R[-c(1, 2, 3), ] - 1)), 0.1) + + epi_snapshot_value(x) }) @@ -321,6 +343,8 @@ test_that("estimate_advantage produces expected results (>2 variants 4 locs)", { ## so ignore fisrt timesteps mean_R <- rowMeans(x$R, dims = 2) expect_lt(max(abs(mean_R[-c(1, 2, 3), ] - 1)), 0.1) + + epi_snapshot_value(x) }) @@ -350,6 +374,8 @@ test_that("estimate_advantage produces expected results (>2 variants 1 loc)", { ## so ignore fisrt timesteps mean_R <- rowMeans(x$R, dims = 2) expect_lt(max(abs(mean_R[-c(1, 2, 3), ] - 1)), 0.1) + + epi_snapshot_value(x) }) @@ -390,6 +416,8 @@ test_that("process_I_multivariant works as expected", { expect_true(all(incid_processed$imported[1, , ] == incid[1, , ])) expect_true(all(incid_processed$local[-1, , ] == incid[-1, , ])) expect_true(all(incid_processed$local[1, , ] == 0)) + + epi_snapshot_value(incid_processed) }) @@ -441,6 +469,8 @@ test_that("estimate_advantage produces expected results (>2var, 1loc, imports)", ## so ignore fisrt timesteps mean_R <- rowMeans(x$R, dims = 2) expect_lt(max(abs(mean_R[-c(1, 2, 3), ] - 1)), 0.1) + + epi_snapshot_value(x) }) @@ -479,6 +509,8 @@ test_that("estimate_advantage produces expected results (>2var, 4loc, imports)", ## so ignore fisrt timesteps mean_R <- rowMeans(x$R, dims = 2) expect_lt(max(abs(mean_R[-c(1, 2, 3), ] - 1)), 0.1) + + epi_snapshot_value(x) }) @@ -512,6 +544,8 @@ test_that("estimate_advantage produces expected results (2var, 1loc, imports)", ## so ignore fisrt timesteps mean_R <- rowMeans(x$R, dims = 2) expect_lt(max(abs(mean_R[-c(1, 2, 3), ] - 1)), 0.1) + + epi_snapshot_value(x) }) @@ -548,6 +582,8 @@ test_that("estimate_advantage produces expected results (2var, 4loc, imports)", ## so ignore fisrt timesteps mean_R <- rowMeans(x$R, dims = 2) expect_lt(max(abs(mean_R[-c(1, 2, 3), ] - 1)), 0.1) + + epi_snapshot_value(x) }) @@ -611,6 +647,7 @@ test_that("estimate_advantage produces expected results (2 var, 2 loc, R_loc1 = expect_equal(mean(x$R[,1,], na.rm = TRUE), 1.1, tolerance = 0.5) expect_equal(mean(x$R[,2,], na.rm = TRUE), 1.5, tolerance = 0.5) + epi_snapshot_value(x) }) test_that("estimate_advantage faster with precompute (2 variants 3 locations)", { @@ -854,6 +891,8 @@ test_that("estimate_advantage uses the correct t_min", { x <- estimate_advantage(incid, si_distr, priors, seed = 1) expect_true(all(is.na(x$R[seq(1, t_min - 1, 1), , ]))) expect_false(anyNA(x$R[seq(t_min, dim(x$R)[1]), , ])) + + epi_snapshot_value(x) }) @@ -880,6 +919,8 @@ test_that("estimate_advantage convergence checks work with >2 variants", { ## that is tested in a different set of tests. expect_length(x$convergence, 2) expect_length(x$diag, 2) + + epi_snapshot_value(x) }) test_that("estimate_advantage produces expected warning message", { From f833b1706e5d646d277183dd07e71326b67df52c Mon Sep 17 00:00:00 2001 From: Steffi LaZerte Date: Wed, 27 May 2026 15:30:51 -0500 Subject: [PATCH 03/31] Clean up - Shouldn't need setup.R any more - Rplots.pdf shouldn't be here --- tests/testthat/Rplots.pdf | Bin 8554 -> 0 bytes tests/testthat/setup.R | 4 ---- 2 files changed, 4 deletions(-) delete mode 100644 tests/testthat/Rplots.pdf delete mode 100644 tests/testthat/setup.R diff --git a/tests/testthat/Rplots.pdf b/tests/testthat/Rplots.pdf deleted file mode 100644 index e9f6757f55b7eed6d1c73d304384af408b5981c9..0000000000000000000000000000000000000000 GIT binary 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zfYPRiuBR$PF$vi%(swS5PrBwtTf+906j&z^6_K z6x0rCjUogI3IeSVm`<#tBMbnvL0ULt&bSs3r^}-*1crn-*<0H|T>(H8%+3k}*;_!6 z2si*}iG*GnVmvJ%5KO0(5NHL%EaeDu1OWNq2$U7n8URH8OcH|O4t91Hf8dXu;5HUW zCwn^!ClmmPutC6~wwTo{enxh@Jh_|v(c=Bd7JeVZfAx5OatWZelO^hB%l8t*kdy}& z_L!6~{NiU_e%UBP?VO<~7{r1Q_`5EE%ZmQl68_0Jev_A93;az>Fn#BH01zKPKmQ+O z=O-=s{~Y-Fz(Sxu9sa|({(gwRi4P%W;{bj>VKLD^_xvZH`d3OAho33^>F}G*T(;%^ zHwR2g{QQ47xFVs}g!}-IFd=4de_a4!At3=FfHmN642&UTmlpv3HwF^MZ05f)K_OvG z8~h)bFs1_k#6-cE+W*5&R2W0@{u|>5Usm{^nApEC5g`mK{D+;0=p`}yZ(k9y%SZfA zOc=vL{~HtK=l@rJqWl=H`A<6#{~zn3kQOjIC=&BQ1OTWFbBAJT3IM9Wtq~aF{FA+E eArL6QFDCnw3tAviKdUba5)vll;84&|B>W%CE Date: Wed, 27 May 2026 15:31:43 -0500 Subject: [PATCH 04/31] Add more tests to fill out gibbs sampling --- tests/testthat/test-samplers.R | 97 +++++++++++++++++++++++++++++++++- 1 file changed, 96 insertions(+), 1 deletion(-) diff --git a/tests/testthat/test-samplers.R b/tests/testthat/test-samplers.R index 101505c9..54b2f434 100644 --- a/tests/testthat/test-samplers.R +++ b/tests/testthat/test-samplers.R @@ -127,6 +127,37 @@ test_that("draw_epsilon produces expected results (>2 variants, 1 location)", { +test_that("draw_epsilon seed", { + n_v <- 3 # 3 variants + n_loc <- 1 # 1 location + T <- 100 # 100 time steps + + priors <- default_priors() + + # constant incidence 10 per day everywhere + incid <- array(10, dim = c(T, n_loc, n_v)) + incid <- process_I_multivariant(incid) + + # arbitrary serial interval + w_v <- c(0, 0.2, 0.5, 0.3) + si_distr <- cbind(w_v, w_v, w_v) + lambda <- compute_lambda(incid, si_distr) + + # Constant reproduction number of 1 + R <- matrix(1, nrow = T, ncol = n_loc) + R[1, ] <- NA # no estimates of R on first time step + + x1 <- sapply(1:1000, function(e) draw_epsilon(R, incid$local, lambda, priors, seed = 1)) + + ## epsilon should be approximately 1 + ## not exactly 1 because of the first few timesteps & because of priors + expect_equal(rowMeans(x1), c(1, 1), tolerance = 0.05) + + epi_snapshot_value(x1) +}) + + + test_that("draw_R produces expected results (2 variants, 4 locations)", { n_v <- 2 # 2 variants n_loc <- 4 # 4 locations @@ -253,7 +284,31 @@ test_that("draw_R produces expected results (>2 variants, 1 location)", { epi_snapshot_value(x) }) -}) + + +test_that("draw_R seed", { + n_v <- 3 # 3 variants + n_loc <- 1 # 1 locations + T <- 100 # 100 time steps + + priors <- default_priors() + + # constant incidence 10 per day everywhere + incid <- array(10, dim = c(T, n_loc, n_v)) + incid <- process_I_multivariant(incid) + + # arbitrary serial interval + w_v <- c(0, 0.2, 0.5, 0.3) + si_distr <- cbind(w_v, w_v, w_v) + lambda <- compute_lambda(incid, si_distr) + + # Epsilon = 1 i.e. no transmission advantage + epsilon <- c(1, 1) + + x <- draw_R(epsilon, incid$local, lambda, priors, t_min = 2L, seed = 1) + + epi_snapshot_value(x) + }) test_that("estimate_advantage produces expected results (2 variants 3 locations)", { @@ -812,6 +867,33 @@ test_that("estimate_advantage faster with precompute (3 variants 1 location)", { expect_lt(max(abs(mean_R2[-c(1, 2, 3), ] - 1)), 0.1) }) +test_that("estimate_advantage checks", { + n_v <- 3 # 3 variants + n_loc <- 1 # 1 locations + T <- 100 # 100 time steps + + priors <- default_priors() + + # constant incidence 10 per day everywhere + incid <- array(10, dim = c(T, n_loc, n_v)) + + # arbitrary serial interval + w_v <- c(0, 0.2, 0.5, 0.3) + si_distr <- cbind(w_v, w_v, w_v) + + expect_error( + estimate_advantage(incid, si_distr, priors, t_min = 90L, t_max = 80L), + "t\\_min is greater than t\\_max" + ) + + mcmc_control <- default_mcmc_controls() + mcmc_control$n_iter <- 10L + expect_error( + estimate_advantage(incid, si_distr, priors, mcmc_control = mcmc_control), + "In mcmc\\_control, n\\_iter must be greater than burnin \\+ thin" + ) +}) + test_that("compute_si_cutoff returns the correct value", { si_1 <- c(0.1, 0.2, 0.3, 0.2, 0.1, 0.03, 0.02, @@ -830,6 +912,14 @@ test_that("compute_si_cutoff returns the correct value", { expect_equal(compute_si_cutoff(si, 0.5), 3L) }) +test_that("compute_si_cutoff warns and normalizes", { + si_1 <- c(1, 2, 3, 2, 1, 0.3, 0.2, 0.1, 0.1, 0.1, 0.2) + si <- cbind(si_1, si_1) + + expect_warning(x <- compute_si_cutoff(si), "Input SI distributions should sum to 1") + expect_equal(x, 7L) +}) + test_that("first_nonzero_incid returns correct value", { incid <- array(0, dim = c(10, 2, 2)) ## Put the non-zero incidence in different @@ -851,6 +941,11 @@ test_that("first_nonzero_incid returns correct value", { expect_equal(first_nonzero_incid(incid), 10L) }) +test_that("first_nonzero_incid warns if all 0", { + incid <- array(0, dim = c(10, 2, 2)) + expect_warning(first_nonzero_incid(incid), "always zero") +}) + test_that("compute_t_min works correctly", { incid <- array(0, dim = c(10, 2, 2)) incid[2, 1, 1] <- 1 From eb9ccfb0addd429e1cc63397cf7000a422165abc Mon Sep 17 00:00:00 2001 From: Steffi LaZerte Date: Thu, 28 May 2026 15:47:56 -0500 Subject: [PATCH 05/31] Convert snapshots to snapshot values - estimate_R() --- tests/testthat/_snaps/estimate_R.md | 1924 +++++++++------------------ tests/testthat/test-estimate_R.R | 8 +- 2 files changed, 660 insertions(+), 1272 deletions(-) diff --git a/tests/testthat/_snaps/estimate_R.md b/tests/testthat/_snaps/estimate_R.md index ae0a36c5..9378c562 100644 --- a/tests/testthat/_snaps/estimate_R.md +++ b/tests/testthat/_snaps/estimate_R.md @@ -1,1280 +1,668 @@ # Example 1 matches saved output - Code - out - Output - $R - t_start t_end date_start date_end Mean(R) Std(R) Quantile.0.025(R) - 1 2 8 2009-04-28 2009-05-04 1.7356089 0.40908694 1.02863184 - 2 3 9 2009-04-29 2009-05-05 1.7490494 0.36470202 1.10874730 - 3 4 10 2009-04-30 2009-05-06 1.5370427 0.30740855 0.99469301 - 4 5 11 2009-05-01 2009-05-07 1.4316392 0.27055438 0.95131387 - 5 6 12 2009-05-02 2009-05-08 1.4223487 0.25143809 0.97288543 - 6 7 13 2009-05-03 2009-05-09 1.6349410 0.25227688 1.17832168 - 7 8 14 2009-05-04 2009-05-10 1.6636221 0.23295369 1.23867595 - 8 9 15 2009-05-05 2009-05-11 1.6156800 0.20686663 1.23586721 - 9 10 16 2009-05-06 2009-05-12 1.4340033 0.17651360 1.10905802 - 10 11 17 2009-05-07 2009-05-13 1.4083724 0.16155139 1.10963727 - 11 12 18 2009-05-08 2009-05-14 1.2477516 0.14219449 0.98470606 - 12 13 19 2009-05-09 2009-05-15 1.0819346 0.12577234 0.84955138 - 13 14 20 2009-05-10 2009-05-16 0.9369319 0.11446445 0.72610935 - 14 15 21 2009-05-11 2009-05-17 0.8194289 0.10759629 0.62222675 - 15 16 22 2009-05-12 2009-05-18 0.6908048 0.10185361 0.50575593 - 16 17 23 2009-05-13 2009-05-19 0.5894141 0.09962917 0.41054853 - 17 18 24 2009-05-14 2009-05-20 0.4989223 0.09978447 0.32287622 - 18 19 25 2009-05-15 2009-05-21 0.4726133 0.10842493 0.28454408 - 19 20 26 2009-05-16 2009-05-22 0.4173489 0.11575175 0.22222070 - 20 21 27 2009-05-17 2009-05-23 0.2976950 0.11251813 0.11968883 - 21 22 28 2009-05-18 2009-05-24 0.2400240 0.12001200 0.06539846 - 22 23 29 2009-05-19 2009-05-25 0.4577916 0.20473063 0.14864369 - 23 24 30 2009-05-20 2009-05-26 0.6754931 0.30208970 0.21933077 - 24 25 31 2009-05-21 2009-05-27 0.8634087 0.38612813 0.28034647 - 25 26 32 2009-05-22 2009-05-28 1.0040161 0.44900963 0.32600128 - Quantile.0.05(R) Quantile.0.25(R) Median(R) Quantile.0.75(R) - 1 1.1218113 1.4450409 1.7035760 1.9913034 - 2 1.1953991 1.4912437 1.7237672 1.9793230 - 3 1.0686828 1.3200764 1.5165981 1.7317432 - 4 1.0175191 1.2410779 1.4146324 1.6036767 - 5 1.0355344 1.2456665 1.4075603 1.5829216 - 6 1.2432610 1.4584617 1.6219838 1.7973033 - 7 1.2998676 1.5011409 1.6527615 1.8142694 - 8 1.2911239 1.4717611 1.6068598 1.7499876 - 9 1.1565305 1.3113322 1.4267674 1.5487891 - 10 1.1535884 1.2963053 1.4022002 1.5137131 - 11 1.0234305 1.1491285 1.2423543 1.3404928 - 12 0.8836967 0.9946576 1.0770650 1.1639048 - 13 0.7569332 0.8573989 0.9322747 1.0113898 - 14 0.6508368 0.7445208 0.8147244 0.8892107 - 15 0.5322318 0.6196538 0.6858055 0.7565087 - 16 0.4356552 0.5195124 0.5838103 0.6532111 - 17 0.3468932 0.4284953 0.4922860 0.5621219 - 18 0.3094859 0.3956839 0.4643482 0.5405441 - 19 0.2468644 0.3345762 0.4066978 0.4885320 - 20 0.1397174 0.2161545 0.2836454 0.3639732 - 21 0.0819873 0.1521344 0.2203457 0.3065963 - 22 0.1803836 0.3084234 0.4276606 0.5744764 - 23 0.2661645 0.4550933 0.6310334 0.8476669 - 24 0.3402089 0.5816958 0.8065807 1.0834797 - 25 0.3956124 0.6764258 0.9379335 1.2599258 - Quantile.0.95(R) Quantile.0.975(R) - 1 2.4587051 2.6244959 - 2 2.3889590 2.5329479 - 3 2.0751554 2.1955178 - 4 1.9037817 2.0085685 - 5 1.8596155 1.9558194 - 6 2.0708249 2.1651730 - 7 2.0644270 2.1502729 - 8 1.9703253 2.0456070 - 9 1.7361602 1.8000616 - 10 1.6842118 1.7421780 - 11 1.4904848 1.5414648 - 12 1.2967845 1.3419870 - 13 1.1328180 1.1742159 - 14 1.0040700 1.0433607 - 15 0.8664328 0.9042566 - 16 0.7622912 0.8001144 - 17 0.6735931 0.7126626 - 18 0.6639414 0.7076205 - 19 0.6241796 0.6729457 - 20 0.5036317 0.5553914 - 21 0.4652659 0.5260890 - 22 0.8380808 0.9377027 - 23 1.2366278 1.3836245 - 24 1.5806457 1.7685354 - 25 1.8380560 2.0565439 - - $method - [1] "non_parametric_si" - - $si_distr - t0 t1 t2 t3 t4 t5 t6 t7 t8 t9 t10 t11 t12 - 0.000 0.233 0.359 0.198 0.103 0.053 0.027 0.014 0.007 0.003 0.002 0.001 0.000 - t13 t14 t15 t16 t17 t18 t19 t20 t21 t22 t23 t24 t25 - 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 - t26 t27 t28 t29 t30 t31 t32 - 0.000 0.000 0.000 0.000 0.000 0.000 0.000 - - $SI.Moments - Mean Std - 1 2.596 1.534531 - - $dates - [1] "2009-04-27" "2009-04-28" "2009-04-29" "2009-04-30" "2009-05-01" - [6] "2009-05-02" "2009-05-03" "2009-05-04" "2009-05-05" "2009-05-06" - [11] "2009-05-07" "2009-05-08" "2009-05-09" "2009-05-10" "2009-05-11" - [16] "2009-05-12" "2009-05-13" "2009-05-14" "2009-05-15" "2009-05-16" - [21] "2009-05-17" "2009-05-18" "2009-05-19" "2009-05-20" "2009-05-21" - [26] "2009-05-22" "2009-05-23" "2009-05-24" "2009-05-25" "2009-05-26" - [31] "2009-05-27" "2009-05-28" - - $I - [1] 1 1 0 2 5 3 3 3 6 2 5 9 13 12 13 11 12 6 6 6 3 1 0 2 0 - [26] 0 0 0 2 0 2 0 - - $I_local - [1] 0 1 0 2 5 3 3 3 6 2 5 9 13 12 13 11 12 6 6 6 3 1 0 2 0 - [26] 0 0 0 2 0 2 0 - - $I_imported - [1] 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 - - attr(,"class") - [1] "estimate_R" + { + "type": "list", + "attributes": { + "names": { + "type": "character", + "attributes": {}, + "value": ["R", "method", "si_distr", "SI.Moments", "dates", "I", "I_local", "I_imported"] + } + }, + "value": [ + { + "type": "list", + "attributes": { + "names": { + "type": "character", + "attributes": {}, + "value": ["t_start", "t_end", "date_start", "date_end", "Mean(R)", "Std(R)", "Quantile.0.025(R)", "Quantile.0.05(R)", "Quantile.0.25(R)", "Median(R)", "Quantile.0.75(R)", "Quantile.0.95(R)", "Quantile.0.975(R)"] + } + }, + "value": [ + { + "type": "double", + "attributes": {}, + "value": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26] + }, + { + "type": "double", + "attributes": {}, + "value": [8, 9, 10, 11, 12, 13, 14, 15, 16, 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1.81426942, 1.74998759, 1.54878908, 1.51371313, 1.34049277, 1.16390481, 1.01138978, 0.88921066, 0.75650867, 0.65321109, 0.56212187, 0.54054409, 0.48853198, 0.36397324, 0.30659631, 0.57447635, 0.84766694, 1.08347966, 1.25992584] + }, + { + "type": "double", + "attributes": {}, + "value": [2.45870505, 2.38895895, 2.07515544, 1.90378168, 1.85961554, 2.07082487, 2.064427, 1.97032529, 1.7361602, 1.68421183, 1.49048476, 1.29678453, 1.13281798, 1.00407002, 0.86643282, 0.76229118, 0.6735931, 0.66394135, 0.62417957, 0.50363169, 0.46526592, 0.83808085, 1.23662781, 1.58064566, 1.83805603] + }, + { + "type": "double", + "attributes": {}, + "value": [2.62449588, 2.53294786, 2.19551784, 2.00856849, 1.95581943, 2.16517301, 2.15027292, 2.04560703, 1.80006161, 1.74217796, 1.54146478, 1.34198696, 1.17421595, 1.04336067, 0.90425657, 0.80011438, 0.7126626, 0.70762052, 0.67294568, 0.55539143, 0.52608899, 0.93770268, 1.38362452, 1.76853543, 2.05654391] + } + ] + }, + { + "type": "character", + "attributes": {}, + "value": ["non_parametric_si"] + }, + { + "type": "double", + "attributes": { + "names": { + "type": "character", + "attributes": {}, + "value": ["t0", "t1", "t2", "t3", "t4", "t5", "t6", "t7", "t8", "t9", "t10", "t11", "t12", "t13", "t14", "t15", "t16", "t17", "t18", "t19", "t20", "t21", "t22", "t23", "t24", "t25", "t26", "t27", "t28", "t29", "t30", "t31", "t32"] + } + }, + "value": [0, 0.233, 0.359, 0.198, 0.103, 0.053, 0.027, 0.014, 0.007, 0.003, 0.002, 0.001, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] + }, + { + "type": "list", + "attributes": { + "names": { + "type": "character", + "attributes": {}, + "value": ["Mean", "Std"] + } + }, + "value": [ + { + "type": "double", + "attributes": {}, + "value": [2.596] + }, + { + "type": "double", + "attributes": {}, + "value": [1.53453055] + } + ] + }, + { + "type": "double", + "attributes": { + "class": { + "type": "character", + "attributes": {}, + "value": ["Date"] + } + }, + "value": [14361, 14362, 14363, 14364, 14365, 14366, 14367, 14368, 14369, 14370, 14371, 14372, 14373, 14374, 14375, 14376, 14377, 14378, 14379, 14380, 14381, 14382, 14383, 14384, 14385, 14386, 14387, 14388, 14389, 14390, 14391, 14392] + }, + { + "type": "double", + "attributes": {}, + "value": [1, 1, 0, 2, 5, 3, 3, 3, 6, 2, 5, 9, 13, 12, 13, 11, 12, 6, 6, 6, 3, 1, 0, 2, 0, 0, 0, 0, 2, 0, 2, 0] + }, + { + "type": "double", + "attributes": {}, + "value": [0, 1, 0, 2, 5, 3, 3, 3, 6, 2, 5, 9, 13, 12, 13, 11, 12, 6, 6, 6, 3, 1, 0, 2, 0, 0, 0, 0, 2, 0, 2, 0] + }, + { + "type": "double", + "attributes": {}, + "value": [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] + } + ] + } # Example 2 matches saved output - Code - out - Output - $R - t_start t_end date_start date_end Mean(R) Std(R) Quantile.0.025(R) - 1 2 8 1 7 0.6413590 0.2424109 0.257859602 - 2 3 9 2 8 0.3999222 0.1788506 0.129853640 - 3 4 10 3 9 0.3956574 0.1769434 0.128468883 - 4 5 11 4 10 0.4170468 0.1865090 0.135413957 - 5 6 12 5 11 0.3515432 0.1757716 0.095783678 - 6 7 13 6 12 0.1976371 0.1397505 0.023934764 - 7 8 14 7 13 0.2561099 0.1810970 0.031016091 - 8 9 15 8 14 0.1854957 0.1854957 0.004696345 - 9 10 16 9 15 0.2764736 0.2764736 0.006999704 - 10 11 17 10 16 0.4085199 0.4085199 0.010342829 - 11 12 18 11 17 0.6246688 0.6246688 0.015815245 - 12 13 19 12 18 1.0775772 1.0775772 0.027281893 - 13 14 20 13 19 1.7523399 1.7523399 0.044365405 - 14 15 21 14 20 2.5831197 2.5831197 0.065398929 - 15 16 22 15 21 3.4013907 3.4013907 0.086115755 - 16 17 23 16 22 4.0476611 4.0476611 0.102477905 - 17 18 24 17 23 4.4742953 4.4742953 0.113279349 - 18 19 25 18 24 4.7233814 4.7233814 0.119585664 - 19 20 26 19 25 4.8583931 4.8583931 0.123003865 - 20 21 27 20 26 9.8571825 6.9700806 1.193750537 - Quantile.0.05(R) Quantile.0.25(R) Median(R) Quantile.0.75(R) - 1 0.301009564 0.46568686 0.6110903 0.7841500 - 2 0.157581299 0.26943560 0.3736000 0.5018568 - 3 0.155900853 0.26656234 0.3696159 0.4965050 - 4 0.164328910 0.28097279 0.3895975 0.5233462 - 5 0.120079970 0.22281862 0.3227220 0.4490461 - 6 0.035116301 0.09499215 0.1658518 0.2660822 - 7 0.045505792 0.12309648 0.2149206 0.3448051 - 8 0.009514686 0.05336379 0.1285758 0.2571517 - 9 0.014181240 0.07953649 0.1916369 0.3832737 - 10 0.020954333 0.11752386 0.2831644 0.5663289 - 11 0.032041322 0.17970602 0.4329874 0.8659749 - 12 0.055272486 0.30999965 0.7469196 1.4938392 - 13 0.089883287 0.50411678 1.2146295 2.4292589 - 14 0.132496720 0.74311724 1.7904822 3.5809643 - 15 0.174468532 0.97851911 2.3576643 4.7153287 - 16 0.207617870 1.16443952 2.8056248 5.6112497 - 17 0.229501345 1.28717454 3.1013452 6.2026903 - 18 0.242277793 1.35883215 3.2739985 6.5479970 - 19 0.249202990 1.39767261 3.3675815 6.7351630 - 20 1.751431640 4.73775012 8.2718863 13.2708950 - Quantile.0.95(R) Quantile.0.975(R) - 1 1.0850325 1.1965445 - 2 0.7321390 0.8191677 - 3 0.7243315 0.8104321 - 4 0.7634891 0.8542443 - 5 0.6814362 0.7705187 - 6 0.4687817 0.5505816 - 7 0.6074752 0.7134764 - 8 0.5556955 0.6842713 - 9 0.8282408 1.0198776 - 10 1.2238163 1.5069808 - 11 1.8713406 2.3043280 - 12 3.2281329 3.9750525 - 13 5.2495412 6.4641707 - 14 7.7383351 9.5288173 - 15 10.1896558 12.5473201 - 16 12.1257088 14.9313337 - 17 13.4037908 16.5051359 - 18 14.1499861 17.4239846 - 19 14.5544451 17.9220266 - 20 23.3805693 27.4603530 - - $method - [1] "parametric_si" - - $si_distr - t0 t1 t2 t3 t4 t5 - 0.000000e+00 2.331721e-01 3.585794e-01 1.981108e-01 1.033427e-01 5.290518e-02 - t6 t7 t8 t9 t10 t11 - 2.682146e-02 1.351620e-02 6.783438e-03 3.394351e-03 1.694674e-03 8.445945e-04 - t12 t13 t14 t15 t16 t17 - 4.203313e-04 2.089422e-04 1.037609e-04 5.148480e-05 2.552773e-05 1.264952e-05 - t18 t19 t20 t21 t22 t23 - 6.264667e-06 3.101068e-06 1.534394e-06 7.589168e-07 3.752319e-07 1.854675e-07 - t24 t25 t26 t27 - 9.164543e-08 4.527299e-08 2.235952e-08 0.000000e+00 - - $SI.Moments - Mean Std - 1 2.599999 1.554876 - - $dates - [1] 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 - [26] 25 26 - - $I - [1] 1 3 1 1 3 4 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 - - $I_local - [1] 0 2 0 0 1 2 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 - - $I_imported - [1] 1 1 1 1 2 2 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 - - attr(,"class") - [1] "estimate_R" + { + "type": "list", + "attributes": { + "names": { + "type": "character", + "attributes": {}, + "value": ["R", "method", "si_distr", "SI.Moments", "dates", "I", "I_local", "I_imported"] + } + }, + "value": [ + { + "type": "list", + "attributes": { + "names": { + "type": "character", + "attributes": {}, + "value": ["t_start", "t_end", "date_start", "date_end", "Mean(R)", "Std(R)", "Quantile.0.025(R)", "Quantile.0.05(R)", "Quantile.0.25(R)", "Median(R)", "Quantile.0.75(R)", "Quantile.0.95(R)", "Quantile.0.975(R)"] + } + }, + "value": [ + { + "type": "double", + "attributes": {}, + "value": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21] + }, + { + "type": "double", + "attributes": {}, + "value": [8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27] + }, + { + "type": "double", + "attributes": {}, + "value": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20] + }, + { + "type": "double", + "attributes": {}, + "value": [7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26] + }, + { + "type": "double", + "attributes": {}, + "value": [0.64135905, 0.39992217, 0.39565741, 0.41704679, 0.35154316, 0.19763705, 0.25610985, 0.18549572, 0.27647356, 0.40851993, 0.62466882, 1.07757723, 1.75233991, 2.58311972, 3.40139065, 4.04766105, 4.47429528, 4.72338141, 4.85839314, 9.85718255] + }, + { + "type": "double", + "attributes": {}, + "value": [0.24241094, 0.17885063, 0.17694337, 0.18650899, 0.17577158, 0.1397505, 0.18109701, 0.18549572, 0.27647356, 0.40851993, 0.62466882, 1.07757723, 1.75233991, 2.58311972, 3.40139065, 4.04766105, 4.47429528, 4.72338141, 4.85839314, 6.97008062] + }, + { + "type": "double", + "attributes": {}, + "value": [0.2578596, 0.12985364, 0.12846888, 0.13541396, 0.09578368, 0.02393476, 0.03101609, 0.00469634, 0.0069997, 0.01034283, 0.01581525, 0.02728189, 0.04436541, 0.06539893, 0.08611576, 0.10247791, 0.11327935, 0.11958566, 0.12300386, 1.19375054] + }, + { + "type": "double", + "attributes": {}, + "value": [0.30100956, 0.1575813, 0.15590085, 0.16432891, 0.12007997, 0.0351163, 0.04550579, 0.00951469, 0.01418124, 0.02095433, 0.03204132, 0.05527249, 0.08988329, 0.13249672, 0.17446853, 0.20761787, 0.22950134, 0.24227779, 0.24920299, 1.75143164] + }, + { + "type": "double", + "attributes": {}, + "value": [0.46568686, 0.2694356, 0.26656234, 0.28097279, 0.22281862, 0.09499215, 0.12309648, 0.05336379, 0.07953649, 0.11752386, 0.17970602, 0.30999965, 0.50411678, 0.74311724, 0.97851911, 1.16443952, 1.28717454, 1.35883215, 1.39767261, 4.73775012] + }, + { + "type": "double", + "attributes": {}, + "value": [0.6110903, 0.3736, 0.36961594, 0.38959751, 0.32272196, 0.16585178, 0.2149206, 0.12857583, 0.19163687, 0.28316444, 0.43298743, 0.74691962, 1.21462947, 1.79048215, 2.35766434, 2.80562485, 3.10134516, 3.27399851, 3.36758151, 8.27188633] + }, + { + "type": "double", + "attributes": {}, + "value": [0.78415002, 0.50185679, 0.49650499, 0.52334624, 0.44904607, 0.26608218, 0.34480512, 0.25715166, 0.38327374, 0.56632888, 0.86597487, 1.49383923, 2.42925894, 3.5809643, 4.71532868, 5.61124969, 6.20269032, 6.54799701, 6.73516302, 13.27089504] + }, + { + "type": "double", + "attributes": {}, + "value": [1.08503252, 0.73213904, 0.72433152, 0.76348914, 0.68143622, 0.46878171, 0.60747522, 0.5556955, 0.82824077, 1.22381634, 1.87134056, 3.22813288, 5.24954123, 7.73833512, 10.18965576, 12.12570885, 13.40379077, 14.14998613, 14.55444513, 23.38056927] + }, + { + "type": "double", + "attributes": {}, + "value": [1.19654455, 0.81916768, 0.81043208, 0.85424433, 0.77051871, 0.55058159, 0.71347639, 0.68427133, 1.01987763, 1.50698078, 2.30432799, 3.9750525, 6.4641707, 9.52881727, 12.5473201, 14.93133369, 16.50513593, 17.42398464, 17.92202664, 27.46035299] + } + ] + }, + { + "type": "character", + "attributes": {}, + "value": ["parametric_si"] + }, + { + "type": "double", + "attributes": { + "names": { + "type": "character", + "attributes": {}, + "value": ["t0", "t1", "t2", "t3", "t4", "t5", "t6", "t7", "t8", "t9", "t10", "t11", "t12", "t13", "t14", "t15", "t16", "t17", "t18", "t19", "t20", "t21", "t22", "t23", "t24", "t25", "t26", "t27"] + } + }, + "value": [0, 0.23317213, 0.3585794, 0.19811083, 0.10334266, 0.05290518, 0.02682146, 0.0135162, 0.00678344, 0.00339435, 0.00169467, 0.00084459, 0.00042033, 0.00020894, 0.00010376, 0.00005148, 0.00002553, 0.00001265, 6.26e-06, 3.1e-06, 1.53e-06, 7.6e-07, 3.8e-07, 1.9e-07, 9e-08, 5e-08, 2e-08, 0] + }, + { + "type": "list", + "attributes": { + "names": { + "type": "character", + "attributes": {}, + "value": ["Mean", "Std"] + } + }, + "value": [ + { + "type": "double", + "attributes": {}, + "value": [2.59999939] + }, + { + "type": "double", + "attributes": {}, + "value": [1.55487586] + } + ] + }, + { + "type": "double", + "attributes": {}, + "value": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26] + }, + { + "type": "double", + "attributes": {}, + "value": [1, 3, 1, 1, 3, 4, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1] + }, + { + "type": "double", + "attributes": {}, + "value": [0, 2, 0, 0, 1, 2, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1] + }, + { + "type": "double", + "attributes": {}, + "value": [1, 1, 1, 1, 2, 2, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] + } + ] + } # Example 3 matches saved output - Code - out - Output - $R - t_start t_end date_start date_end Mean(R) Std(R) Quantile.0.025(R) - 1 2 8 2009-04-28 2009-05-04 1.7357977 0.40913143 1.02874370 - 2 3 9 2009-04-29 2009-05-05 1.7491678 0.36472669 1.10882231 - 3 4 10 2009-04-30 2009-05-06 1.5370581 0.30741163 0.99470299 - 4 5 11 2009-05-01 2009-05-07 1.4318389 0.27059213 0.95144658 - 5 6 12 2009-05-02 2009-05-08 1.4227247 0.25150457 0.97314263 - 6 7 13 2009-05-03 2009-05-09 1.6353733 0.25234358 1.17863324 - 7 8 14 2009-05-04 2009-05-10 1.6639845 0.23300443 1.23894574 - 8 9 15 2009-05-05 2009-05-11 1.6161467 0.20692639 1.23622420 - 9 10 16 2009-05-06 2009-05-12 1.4344239 0.17656537 1.10938331 - 10 11 17 2009-05-07 2009-05-13 1.4087217 0.16159146 1.10991245 - 11 12 18 2009-05-08 2009-05-14 1.2479472 0.14221678 0.98486038 - 12 13 19 2009-05-09 2009-05-15 1.0821477 0.12579711 0.84971868 - 13 14 20 2009-05-10 2009-05-16 0.9371671 0.11449319 0.72629165 - 14 15 21 2009-05-11 2009-05-17 0.8196564 0.10762616 0.62239946 - 15 16 22 2009-05-12 2009-05-18 0.6910352 0.10188759 0.50592464 - 16 17 23 2009-05-13 2009-05-19 0.5896765 0.09967353 0.41073132 - 17 18 24 2009-05-14 2009-05-20 0.4992206 0.09984413 0.32306927 - 18 19 25 2009-05-15 2009-05-21 0.4729631 0.10850519 0.28475471 - 19 20 26 2009-05-16 2009-05-22 0.4176319 0.11583024 0.22237137 - 20 21 27 2009-05-17 2009-05-23 0.2978682 0.11258359 0.11975846 - 21 22 28 2009-05-18 2009-05-24 0.2400880 0.12004401 0.06541591 - 22 23 29 2009-05-19 2009-05-25 0.4573477 0.20453212 0.14849956 - 23 24 30 2009-05-20 2009-05-26 0.6734382 0.30117074 0.21866356 - 24 25 31 2009-05-21 2009-05-27 0.8591897 0.38424130 0.27897655 - 25 26 32 2009-05-22 2009-05-28 0.9975352 0.44611130 0.32389696 - Quantile.0.05(R) Quantile.0.25(R) Median(R) Quantile.0.75(R) - 1 1.12193325 1.4451981 1.7037612 1.9915200 - 2 1.19547993 1.4913446 1.7238839 1.9794569 - 3 1.06869352 1.3200896 1.5166133 1.7317605 - 4 1.01766106 1.2412511 1.4148298 1.6039005 - 5 1.03580815 1.2459958 1.4079324 1.5833401 - 6 1.24358968 1.4588473 1.6224126 1.7977785 - 7 1.30015068 1.5014679 1.6531215 1.8146646 - 8 1.29149686 1.4721862 1.6073240 1.7504931 - 9 1.15686977 1.3117168 1.4271858 1.5492434 - 10 1.15387452 1.2966267 1.4025479 1.5140885 - 11 1.02359091 1.1493086 1.2425490 1.3407028 - 12 0.88387071 0.9948535 1.0772771 1.1641340 - 13 0.75712322 0.8576141 0.9325088 1.0116437 - 14 0.65101748 0.7447274 0.8149506 0.8894575 - 15 0.53240938 0.6198605 0.6860342 0.7567610 - 16 0.43584912 0.5197437 0.5840702 0.6535019 - 17 0.34710064 0.4287515 0.4925804 0.5624580 - 18 0.30971499 0.3959768 0.4646919 0.5409442 - 19 0.24703176 0.3348031 0.4069735 0.4888632 - 20 0.13979872 0.2162803 0.2838104 0.3641850 - 21 0.08200917 0.1521750 0.2204045 0.3066781 - 22 0.18020868 0.3081243 0.4272459 0.5739193 - 23 0.26535481 0.4537089 0.6291137 0.8450883 - 24 0.33854643 0.5788533 0.8026393 1.0781852 - 25 0.39305871 0.6720595 0.9318792 1.2517931 - Quantile.0.95(R) Quantile.0.975(R) - 1 2.4589724 2.6247813 - 2 2.3891206 2.5331192 - 3 2.0751763 2.1955399 - 4 1.9040473 2.0088487 - 5 1.8601072 1.9563365 - 6 2.0713724 2.1657455 - 7 2.0648767 2.1507413 - 8 1.9708944 2.0461979 - 9 1.7366694 1.8005896 - 10 1.6846295 1.7426100 - 11 1.4907183 1.5417063 - 12 1.2970399 1.3422512 - 13 1.1331024 1.1745108 - 14 1.0043487 1.0436503 - 15 0.8667218 0.9045582 - 16 0.7626306 0.8004706 - 17 0.6739958 0.7130887 - 18 0.6644328 0.7081443 - 19 0.6246028 0.6734020 - 20 0.5039247 0.5557145 - 21 0.4653900 0.5262293 - 22 0.8372682 0.9367934 - 23 1.2328660 1.3794155 - 24 1.5729218 1.7598935 - 25 1.8261915 2.0432690 - - $method - [1] "parametric_si" - - $si_distr - t0 t1 t2 t3 t4 t5 - 0.000000e+00 2.331721e-01 3.585794e-01 1.981108e-01 1.033427e-01 5.290518e-02 - t6 t7 t8 t9 t10 t11 - 2.682146e-02 1.351620e-02 6.783438e-03 3.394351e-03 1.694674e-03 8.445945e-04 - t12 t13 t14 t15 t16 t17 - 4.203313e-04 2.089422e-04 1.037609e-04 5.148480e-05 2.552773e-05 1.264952e-05 - t18 t19 t20 t21 t22 t23 - 6.264667e-06 3.101068e-06 1.534394e-06 7.589168e-07 3.752319e-07 1.854675e-07 - t24 t25 t26 t27 t28 t29 - 9.164543e-08 4.527299e-08 2.235952e-08 1.104051e-08 5.450375e-09 2.690170e-09 - t30 t31 t32 - 1.327568e-09 6.550364e-10 0.000000e+00 - - $SI.Moments - Mean Std - 1 2.6 1.55488 - - $dates - [1] "2009-04-27" "2009-04-28" "2009-04-29" "2009-04-30" "2009-05-01" - [6] "2009-05-02" "2009-05-03" "2009-05-04" "2009-05-05" "2009-05-06" - [11] "2009-05-07" "2009-05-08" "2009-05-09" "2009-05-10" "2009-05-11" - [16] "2009-05-12" "2009-05-13" "2009-05-14" "2009-05-15" "2009-05-16" - [21] "2009-05-17" "2009-05-18" "2009-05-19" "2009-05-20" "2009-05-21" - [26] "2009-05-22" "2009-05-23" "2009-05-24" "2009-05-25" "2009-05-26" - [31] "2009-05-27" "2009-05-28" - - $I - [1] 1 1 0 2 5 3 3 3 6 2 5 9 13 12 13 11 12 6 6 6 3 1 0 2 0 - [26] 0 0 0 2 0 2 0 - - $I_local - [1] 0 1 0 2 5 3 3 3 6 2 5 9 13 12 13 11 12 6 6 6 3 1 0 2 0 - [26] 0 0 0 2 0 2 0 - - $I_imported - [1] 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 - - attr(,"class") - [1] "estimate_R" + { + "type": "list", + "attributes": { + "names": { + "type": "character", + "attributes": {}, + "value": ["R", "method", "si_distr", "SI.Moments", "dates", "I", "I_local", "I_imported"] + } + }, + "value": [ + { + "type": "list", + "attributes": { + "names": { + "type": "character", + "attributes": {}, + "value": ["t_start", "t_end", "date_start", "date_end", "Mean(R)", "Std(R)", "Quantile.0.025(R)", "Quantile.0.05(R)", "Quantile.0.25(R)", "Median(R)", "Quantile.0.75(R)", "Quantile.0.95(R)", "Quantile.0.975(R)"] + } + }, + "value": [ + { + "type": "double", + "attributes": {}, + "value": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26] + }, + { + "type": "double", + "attributes": {}, + "value": [8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32] + }, + { + "type": "double", + "attributes": { + "class": { + "type": "character", + "attributes": {}, + "value": ["Date"] + } + }, + "value": [14362, 14363, 14364, 14365, 14366, 14367, 14368, 14369, 14370, 14371, 14372, 14373, 14374, 14375, 14376, 14377, 14378, 14379, 14380, 14381, 14382, 14383, 14384, 14385, 14386] + }, + { + "type": "double", + "attributes": { + "class": { + "type": "character", + "attributes": {}, + "value": ["Date"] + } + }, + "value": [14368, 14369, 14370, 14371, 14372, 14373, 14374, 14375, 14376, 14377, 14378, 14379, 14380, 14381, 14382, 14383, 14384, 14385, 14386, 14387, 14388, 14389, 14390, 14391, 14392] + }, + { + "type": "double", + "attributes": {}, + "value": [1.73579766, 1.74916776, 1.53705814, 1.43183894, 1.42272468, 1.63537331, 1.66398448, 1.61614674, 1.43442387, 1.40872167, 1.24794716, 1.08214768, 0.93716713, 0.81965639, 0.69103523, 0.58967655, 0.49922064, 0.47296315, 0.41763185, 0.29786818, 0.24008802, 0.45734772, 0.67343824, 0.85918968, 0.99753519] + }, + { + "type": "double", + "attributes": {}, + "value": [0.40913143, 0.36472669, 0.30741163, 0.27059213, 0.25150457, 0.25234358, 0.23300443, 0.20692639, 0.17656537, 0.16159146, 0.14221678, 0.12579711, 0.11449319, 0.10762616, 0.10188759, 0.09967353, 0.09984413, 0.10850519, 0.11583024, 0.11258359, 0.12004401, 0.20453212, 0.30117074, 0.3842413, 0.4461113] + }, + { + "type": "double", + "attributes": {}, + "value": [1.0287437, 1.10882231, 0.99470299, 0.95144658, 0.97314263, 1.17863324, 1.23894574, 1.2362242, 1.10938331, 1.10991245, 0.98486038, 0.84971868, 0.72629165, 0.62239946, 0.50592464, 0.41073132, 0.32306927, 0.28475471, 0.22237137, 0.11975846, 0.06541591, 0.14849956, 0.21866356, 0.27897655, 0.32389696] + }, + { + "type": "double", + "attributes": {}, + "value": [1.12193325, 1.19547993, 1.06869352, 1.01766106, 1.03580815, 1.24358968, 1.30015068, 1.29149686, 1.15686977, 1.15387452, 1.02359091, 0.88387071, 0.75712322, 0.65101748, 0.53240938, 0.43584912, 0.34710064, 0.30971499, 0.24703176, 0.13979872, 0.08200917, 0.18020868, 0.26535481, 0.33854643, 0.39305871] + }, + { + "type": "double", + "attributes": {}, + "value": [1.44519809, 1.49134461, 1.32008959, 1.24125108, 1.24599581, 1.4588473, 1.50146785, 1.4721862, 1.31171684, 1.29662673, 1.14930856, 0.99485346, 0.85761413, 0.74472745, 0.61986052, 0.51974369, 0.42875149, 0.39597676, 0.33480311, 0.21628025, 0.152175, 0.30812434, 0.45370886, 0.57885334, 0.67205949] + }, + { + "type": "double", + "attributes": {}, + "value": [1.70376124, 1.72388385, 1.51661332, 1.41482979, 1.40793239, 1.62241263, 1.6531215, 1.60732397, 1.42718584, 1.40254791, 1.24254897, 1.07727706, 0.93250875, 0.81495056, 0.68603423, 0.58407019, 0.49258037, 0.46469192, 0.40697354, 0.28381038, 0.22040445, 0.4272459, 0.62911373, 0.80263934, 0.9318792] + }, + { + "type": "double", + "attributes": {}, + "value": [1.99151998, 1.97945687, 1.73176052, 1.60390045, 1.58334008, 1.79777853, 1.81466459, 1.75049309, 1.54924336, 1.51408853, 1.34070284, 1.16413401, 1.01164371, 0.88945748, 0.75676103, 0.65350191, 0.56245796, 0.54094422, 0.48886323, 0.36418499, 0.30667809, 0.57391931, 0.84508832, 1.07818522, 1.25179309] + }, + { + "type": "double", + "attributes": {}, + "value": [2.45897244, 2.38912057, 2.07517625, 1.90404726, 1.86010717, 2.07137241, 2.06487665, 1.97089443, 1.73666944, 1.6846295, 1.49071834, 1.29703991, 1.1331024, 1.00434871, 0.86672185, 0.76263058, 0.67399585, 0.66443282, 0.62460279, 0.50392469, 0.46539002, 0.8372682, 1.23286595, 1.57292181, 1.82619148] + }, + { + "type": "double", + "attributes": {}, + "value": [2.62478131, 2.53311922, 2.19553985, 2.00884868, 1.95633649, 2.1657455, 2.15074127, 2.04619792, 1.80058959, 1.74261002, 1.54170635, 1.34225124, 1.17451076, 1.04365027, 0.90455821, 0.80047061, 0.7130887, 0.70814432, 0.67340197, 0.55571454, 0.52622931, 0.93679344, 1.37941549, 1.75989345, 2.04326903] + } + ] + }, + { + "type": "character", + "attributes": {}, + "value": ["parametric_si"] + }, + { + "type": "double", + "attributes": { + "names": { + "type": "character", + "attributes": {}, + "value": ["t0", "t1", "t2", "t3", "t4", "t5", "t6", "t7", "t8", "t9", "t10", "t11", "t12", "t13", "t14", "t15", "t16", "t17", "t18", "t19", "t20", "t21", "t22", "t23", "t24", "t25", "t26", "t27", "t28", "t29", "t30", "t31", "t32"] + } + }, + "value": [0, 0.23317213, 0.3585794, 0.19811083, 0.10334266, 0.05290518, 0.02682146, 0.0135162, 0.00678344, 0.00339435, 0.00169467, 0.00084459, 0.00042033, 0.00020894, 0.00010376, 0.00005148, 0.00002553, 0.00001265, 6.26e-06, 3.1e-06, 1.53e-06, 7.6e-07, 3.8e-07, 1.9e-07, 9e-08, 5e-08, 2e-08, 1e-08, 1e-08, 0, 0, 0, 0] + }, + { + "type": "list", + "attributes": { + "names": { + "type": "character", + "attributes": {}, + "value": ["Mean", "Std"] + } + }, + "value": [ + { + "type": "double", + "attributes": {}, + "value": [2.59999998] + }, + { + "type": "double", + "attributes": {}, + "value": [1.55488015] + } + ] + }, + { + "type": "double", + "attributes": { + "class": { + "type": "character", + "attributes": {}, + "value": ["Date"] + } + }, + "value": [14361, 14362, 14363, 14364, 14365, 14366, 14367, 14368, 14369, 14370, 14371, 14372, 14373, 14374, 14375, 14376, 14377, 14378, 14379, 14380, 14381, 14382, 14383, 14384, 14385, 14386, 14387, 14388, 14389, 14390, 14391, 14392] + }, + { + "type": "double", + "attributes": {}, + "value": [1, 1, 0, 2, 5, 3, 3, 3, 6, 2, 5, 9, 13, 12, 13, 11, 12, 6, 6, 6, 3, 1, 0, 2, 0, 0, 0, 0, 2, 0, 2, 0] + }, + { + "type": "double", + "attributes": {}, + "value": [0, 1, 0, 2, 5, 3, 3, 3, 6, 2, 5, 9, 13, 12, 13, 11, 12, 6, 6, 6, 3, 1, 0, 2, 0, 0, 0, 0, 2, 0, 2, 0] + }, + { + "type": "double", + "attributes": {}, + "value": [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] + } + ] + } # Example 4 matches saved output - Code - out - Output - $R - t_start t_end date_start date_end Mean(R) Std(R) Quantile.0.025(R) - 1 2 8 2009-04-28 2009-05-04 1.8873241 0.6972105 0.90698577 - 2 3 9 2009-04-29 2009-05-05 1.8321097 0.5153371 1.02631534 - 3 4 10 2009-04-30 2009-05-06 1.5726539 0.4107172 0.91925497 - 4 5 11 2009-05-01 2009-05-07 1.4780461 0.3429376 0.91178361 - 5 6 12 2009-05-02 2009-05-08 1.4484020 0.3076305 0.93072780 - 6 7 13 2009-05-03 2009-05-09 1.6578168 0.3096693 1.12730374 - 7 8 14 2009-05-04 2009-05-10 1.6792380 0.3366632 1.09833686 - 8 9 15 2009-05-05 2009-05-11 1.6564870 0.3409534 1.09358003 - 9 10 16 2009-05-06 2009-05-12 1.4729814 0.2938076 0.98102847 - 10 11 17 2009-05-07 2009-05-13 1.4465064 0.2403680 1.04658752 - 11 12 18 2009-05-08 2009-05-14 1.2651883 0.1980674 0.92089130 - 12 13 19 2009-05-09 2009-05-15 1.1023896 0.1533957 0.83161401 - 13 14 20 2009-05-10 2009-05-16 0.9511777 0.1210106 0.73371607 - 14 15 21 2009-05-11 2009-05-17 0.8292920 0.1120904 0.62228842 - 15 16 22 2009-05-12 2009-05-18 0.6984467 0.1144250 0.49750263 - 16 17 23 2009-05-13 2009-05-19 0.6022503 0.1268094 0.39254095 - 17 18 24 2009-05-14 2009-05-20 0.5124873 0.1368321 0.29713537 - 18 19 25 2009-05-15 2009-05-21 0.4916119 0.1577686 0.25657041 - 19 20 26 2009-05-16 2009-05-22 0.4327667 0.1615940 0.19851290 - 20 21 27 2009-05-17 2009-05-23 0.3139704 0.1518737 0.10761438 - 21 22 28 2009-05-18 2009-05-24 0.2669934 0.1789874 0.05931812 - 22 23 29 2009-05-19 2009-05-25 0.5220287 0.3636535 0.11992330 - 23 24 30 2009-05-20 2009-05-26 0.6784284 0.3733937 0.18144389 - 24 25 31 2009-05-21 2009-05-27 0.8532121 0.4261440 0.25295410 - 25 26 32 2009-05-22 2009-05-28 0.9738995 0.4623334 0.30199442 - Quantile.0.05(R) Quantile.0.25(R) Median(R) Quantile.0.75(R) - 1 1.00319828 1.3863986 1.7463954 2.2544726 - 2 1.12006747 1.4653858 1.7556111 2.1278730 - 3 0.99274130 1.2758472 1.5269187 1.8180021 - 4 0.97628584 1.2323563 1.4469190 1.6911924 - 5 0.99628070 1.2267350 1.4186807 1.6392647 - 6 1.19637383 1.4386490 1.6315923 1.8465898 - 7 1.16956408 1.4310103 1.6564079 1.9012149 - 8 1.15748490 1.3994881 1.6266379 1.8732233 - 9 1.04391585 1.2543043 1.4426923 1.6583391 - 10 1.09740208 1.2712795 1.4211555 1.5942230 - 11 0.96509940 1.1235171 1.2511130 1.3910518 - 12 0.87202211 0.9941303 1.0917930 1.1984578 - 13 0.76295879 0.8673023 0.9451165 1.0302624 - 14 0.65116400 0.7511913 0.8237088 0.9027583 - 15 0.52365192 0.6186482 0.6900770 0.7727973 - 16 0.41745746 0.5116862 0.5889917 0.6782450 - 17 0.32461798 0.4131051 0.4935481 0.5925992 - 18 0.28003138 0.3776250 0.4653162 0.5786912 - 19 0.22403772 0.3158716 0.4045424 0.5178140 - 20 0.12487667 0.2060081 0.2854834 0.3874172 - 21 0.07484891 0.1439744 0.2246876 0.3392199 - 22 0.15088796 0.2838583 0.4295091 0.6506849 - 23 0.22384278 0.4081694 0.6002330 0.8678315 - 24 0.30659273 0.5436978 0.7728316 1.0795506 - 25 0.36604985 0.6354974 0.8942639 1.2277183 - Quantile.0.95(R) Quantile.0.975(R) - 1 3.2377956 3.6126949 - 2 2.7833576 3.0390217 - 3 2.3123865 2.4847322 - 4 2.1010335 2.2430388 - 5 1.9994660 2.1129052 - 6 2.2118241 2.3422591 - 7 2.2752929 2.3785934 - 8 2.2693166 2.4026326 - 9 1.9993020 2.1157440 - 10 1.8864297 1.9815759 - 11 1.6126349 1.6915830 - 12 1.3731259 1.4276111 - 13 1.1616742 1.2084007 - 14 1.0211347 1.0636335 - 15 0.8979939 0.9412680 - 16 0.8325864 0.8835215 - 17 0.7703307 0.8314014 - 18 0.7950972 0.8731328 - 19 0.7409126 0.8277608 - 20 0.6084116 0.7017960 - 21 0.6077974 0.7445009 - 22 1.2113522 1.4990388 - 23 1.3896768 1.6039553 - 24 1.6523701 1.8895694 - 25 1.8439356 2.0744782 - - $method - [1] "uncertain_si" - - $si_distr - t0 t1 t2 t3 t4 t5 - [1,] 0 5.402991e-01 0.248081450 0.093539784 0.048408162 0.027316804 - [2,] 0 7.618892e-01 0.109832213 0.040134882 0.023590501 0.015660084 - [3,] 0 6.074399e-02 0.343316237 0.335013061 0.168159796 0.063658618 - [4,] 0 1.661246e-01 0.312794848 0.211069136 0.129244313 0.076574400 - [5,] 0 6.209182e-02 0.298668406 0.301979461 0.182588713 0.089528622 - [6,] 0 1.592947e-02 0.146797995 0.255786406 0.233961893 0.160221306 - [7,] 0 6.285767e-01 0.181573656 0.068882363 0.039042861 0.024610376 - [8,] 0 2.474145e-01 0.358711896 0.192150970 0.099237246 0.050649998 - [9,] 0 8.468402e-02 0.263584094 0.239452705 0.166124355 0.104231990 - [10,] 0 1.568817e-01 0.302370649 0.210321907 0.132526203 0.080739798 - [11,] 0 5.930474e-02 0.269266442 0.281676672 0.187676457 0.104333442 - [12,] 0 7.339422e-02 0.296654300 0.281850253 0.175402574 0.092417238 - [13,] 0 9.912143e-02 0.484671984 0.311702766 0.085308668 0.016240316 - [14,] 0 4.893516e-01 0.264509163 0.106014943 0.056281448 0.032314461 - [15,] 0 1.034571e-02 0.137969660 0.283106507 0.260775465 0.164049575 - [16,] 0 1.169662e-01 0.335093690 0.257656203 0.147288127 0.075484824 - [17,] 0 9.280241e-01 0.035200226 0.011780005 0.006755648 0.004426863 - [18,] 0 3.912758e-01 0.325181857 0.138193469 0.068504773 0.035532131 - [19,] 0 6.099079e-02 0.238368387 0.247982621 0.181777539 0.116291582 - [20,] 0 2.640286e-01 0.372017480 0.188326908 0.091633894 0.044022148 - [21,] 0 1.063318e-01 0.293397051 0.240845470 0.155381424 0.091811288 - [22,] 0 4.639328e-01 0.329034132 0.116419872 0.049403816 0.022081120 - [23,] 0 2.009325e-01 0.317964573 0.196742559 0.117486761 0.069343737 - [24,] 0 3.752934e-01 0.291653197 0.135491038 0.076261464 0.045406080 - [25,] 0 6.637896e-02 0.330763498 0.317306984 0.171289048 0.072941715 - [26,] 0 3.597256e-02 0.327988493 0.385732584 0.179739866 0.054356132 - [27,] 0 8.667651e-02 0.227819246 0.204201927 0.153869039 0.108909950 - [28,] 0 2.406965e-01 0.445881953 0.204612248 0.073469764 0.024272108 - [29,] 0 7.548527e-02 0.304447274 0.284718449 0.173105795 0.088837107 - [30,] 0 3.259756e-01 0.311491060 0.153022149 0.085461676 0.049613404 - [31,] 0 1.359301e-01 0.402757830 0.268619662 0.120654230 0.046756618 - [32,] 0 1.127706e-02 0.342676698 0.504790379 0.129090184 0.011583647 - [33,] 0 1.221138e-02 0.138045272 0.265848927 0.247966289 0.165258504 - [34,] 0 4.766449e-02 0.307747352 0.339335915 0.188295427 0.077597873 - [35,] 0 2.129437e-02 0.252286488 0.382076108 0.226192250 0.085219674 - [36,] 0 8.930005e-01 0.050889760 0.017465049 0.010139600 0.006711376 - [37,] 0 4.230257e-01 0.309641804 0.127625863 0.063883259 0.033822387 - [38,] 0 1.404530e-01 0.389484173 0.261229528 0.124253930 0.052086546 - [39,] 0 4.713583e-01 0.294365010 0.113673908 0.055627329 0.029104236 - [40,] 0 6.449736e-05 0.038383617 0.347416470 0.437899206 0.152036401 - [41,] 0 1.079492e-01 0.305908605 0.248051477 0.154535448 0.087400472 - [42,] 0 1.717820e-02 0.183956913 0.313808953 0.247829050 0.136878780 - [43,] 0 1.512995e-01 0.331692572 0.229778765 0.133780839 0.073299352 - [44,] 0 5.980897e-01 0.194649114 0.075435365 0.042895600 0.027022204 - [45,] 0 5.125028e-02 0.226033190 0.252262001 0.189851676 0.122173326 - [46,] 0 1.698762e-02 0.145392308 0.246515350 0.227295959 0.159970598 - [47,] 0 8.636418e-01 0.074867145 0.023859005 0.012754693 0.007754618 - [48,] 0 8.376267e-01 0.089880230 0.028747274 0.015242069 0.009169871 - [49,] 0 5.208989e-01 0.262796276 0.099176715 0.050222339 0.027579871 - [50,] 0 6.978201e-01 0.163216639 0.056397750 0.030013814 0.017864584 - [51,] 0 6.919751e-01 0.142114887 0.053567287 0.031443539 0.020710915 - [52,] 0 1.300440e-01 0.291882341 0.220650672 0.143258054 0.088178485 - [53,] 0 2.803600e-01 0.285828908 0.155636300 0.095923571 0.061403856 - [54,] 0 4.665438e-01 0.313065654 0.115877730 0.052819171 0.025542733 - [55,] 0 1.859703e-03 0.077620819 0.284348429 0.323817688 0.195453947 - [56,] 0 2.633962e-01 0.381972042 0.190286843 0.089242946 0.041046200 - [57,] 0 3.276127e-01 0.359329650 0.162446463 0.077335903 0.037406688 - [58,] 0 8.501239e-02 0.317929383 0.281255628 0.165501089 0.083129390 - [59,] 0 5.926028e-01 0.194750359 0.076167041 0.043641439 0.027694153 - [60,] 0 2.940891e-01 0.361684531 0.174772875 0.085756548 0.042281016 - [61,] 0 3.523432e-01 0.304094086 0.144212334 0.080206558 0.046771779 - [62,] 0 2.554792e-01 0.359247041 0.189018711 0.096861979 0.049217320 - [63,] 0 5.110449e-01 0.283072909 0.102876068 0.048769979 0.024890160 - [64,] 0 1.445973e-01 0.391027703 0.258461001 0.122412012 0.051321652 - [65,] 0 5.464286e-03 0.214516306 0.485300723 0.239500307 0.048796024 - [66,] 0 1.567062e-02 0.310838817 0.464081932 0.174402490 0.031054836 - [67,] 0 2.853445e-01 0.387972235 0.181722961 0.081319729 0.035864007 - [68,] 0 5.268457e-01 0.263529276 0.097918674 0.048870388 0.026453757 - [69,] 0 1.504487e-05 0.027093899 0.343974702 0.470683240 0.142479249 - [70,] 0 2.758239e-01 0.446319865 0.185401368 0.063266580 0.020231835 - [71,] 0 5.913448e-01 0.226643688 0.081218088 0.041435106 0.023239325 - [72,] 0 7.818000e-01 0.160494966 0.035941877 0.012787398 0.005099323 - [73,] 0 1.068488e-01 0.325980628 0.261900783 0.153272159 0.079703298 - [74,] 0 2.358618e-01 0.433761083 0.207683733 0.079725080 0.028397207 - [75,] 0 9.651494e-01 0.016867597 0.005607677 0.003232335 0.002133526 - [76,] 0 1.101849e-02 0.170668135 0.341134951 0.268650103 0.133770757 - [77,] 0 9.709717e-01 0.010309510 0.003764097 0.002362649 0.001697196 - [78,] 0 9.272243e-02 0.329839702 0.279411159 0.159292219 0.077976900 - [79,] 0 7.298946e-02 0.248666799 0.240362194 0.172570390 0.110694724 - [80,] 0 4.841399e-01 0.293681310 0.110338952 0.052833317 0.027081772 - [81,] 0 9.568091e-01 0.018950117 0.006583074 0.003938276 0.002695757 - [82,] 0 1.786307e-01 0.348356958 0.221043802 0.121941248 0.064128304 - [83,] 0 1.003938e-01 0.324955189 0.267866116 0.156508993 0.080308944 - [84,] 0 3.656748e-01 0.333122641 0.146678953 0.073205514 0.037927457 - [85,] 0 1.253197e-01 0.284774206 0.219141323 0.144833632 0.090739423 - [86,] 0 4.374486e-04 0.060974480 0.338771960 0.385190695 0.167497699 - [87,] 0 4.474190e-02 0.235931932 0.278021218 0.201066558 0.118939670 - [88,] 0 9.797597e-01 0.007504046 0.002711326 0.001688910 0.001204508 - [89,] 0 4.523806e-01 0.249971786 0.109508819 0.063634590 0.040035210 - [90,] 0 5.481036e-01 0.267587761 0.092976750 0.043448836 0.022015916 - [91,] 0 6.753530e-01 0.184178725 0.061987558 0.031487309 0.017805544 - [92,] 0 5.639653e-02 0.326600757 0.333278462 0.176965717 0.071176323 - [93,] 0 3.981979e-03 0.151647421 0.415473718 0.299608410 0.102446674 - [94,] 0 8.470814e-01 0.068524545 0.024497598 0.014571812 0.009854142 - [95,] 0 7.764920e-01 0.114442947 0.039503722 0.021986700 0.013818920 - [96,] 0 4.411681e-01 0.285308297 0.119625881 0.063498316 0.036048338 - [97,] 0 2.740584e-03 0.080062137 0.257874203 0.297599221 0.201144197 - [98,] 0 1.106711e-01 0.297134258 0.239562804 0.153154758 0.090006955 - [99,] 0 2.964391e-01 0.324928523 0.165352077 0.091217247 0.051587826 - [100,] 0 3.458534e-01 0.317588073 0.149289404 0.080315571 0.044990860 - t6 t7 t8 t9 t10 - [1,] 0.0161121422 9.765091e-03 6.028565e-03 3.771906e-03 2.384053e-03 - [2,] 0.0110619466 8.118102e-03 6.114409e-03 4.692846e-03 3.653687e-03 - [3,] 0.0206946095 6.118278e-03 1.695408e-03 4.482451e-04 1.143624e-04 - [4,] 0.0445925910 2.569226e-02 1.469533e-02 8.361040e-03 4.738022e-03 - [5,] 0.0392207335 1.601559e-02 6.233246e-03 2.342799e-03 8.575340e-04 - [6,] 0.0934837425 4.929624e-02 2.424781e-02 1.133679e-02 5.099497e-03 - [7,] 0.0163787686 1.127049e-02 7.933415e-03 5.677097e-03 4.113581e-03 - [8,] 0.0256935979 1.298466e-02 6.545176e-03 3.293089e-03 1.654521e-03 - [9,] 0.0619978687 3.567099e-02 2.006264e-02 1.109912e-02 6.063695e-03 - [10,] 0.0483287932 2.861369e-02 1.681523e-02 9.828312e-03 5.720950e-03 - [11,] 0.0526236602 2.497808e-02 1.137364e-02 5.024896e-03 2.169656e-03 - [12,] 0.0445463501 2.031363e-02 8.918930e-03 3.809664e-03 1.593615e-03 - [13,] 0.0025481799 3.548405e-04 4.557436e-05 5.521600e-06 6.401255e-07 - [14,] 0.0193134344 1.183140e-02 7.370439e-03 4.647599e-03 2.957805e-03 - [15,] 0.0832947219 3.691383e-02 1.489591e-02 5.613704e-03 2.008275e-03 - [16,] 0.0365167554 1.704427e-02 7.763643e-03 3.474237e-03 1.533956e-03 - [17,] 0.0031046768 2.270121e-03 1.707621e-03 1.311182e-03 1.022620e-03 - [18,] 0.0188634369 1.015847e-02 5.523991e-03 3.025054e-03 1.665449e-03 - [19,] 0.0691681243 3.931041e-02 2.165674e-02 1.166396e-02 6.174799e-03 - [20,] 0.0210099287 9.986544e-03 4.733779e-03 2.239405e-03 1.057789e-03 - [21,] 0.0517854815 2.837784e-02 1.524722e-02 8.075748e-03 4.231067e-03 - [22,] 0.0101339273 4.726933e-03 2.229242e-03 1.059721e-03 5.068107e-04 - [23,] 0.0406800432 2.377501e-02 1.385945e-02 8.064234e-03 4.685610e-03 - [24,] 0.0278287478 1.736263e-02 1.096486e-02 6.985708e-03 4.480410e-03 - [25,] 0.0273430680 9.467011e-03 3.104772e-03 9.789380e-04 2.995797e-04 - [26,] 0.0129556415 2.660097e-03 4.932931e-04 8.496424e-05 1.383753e-05 - [27,] 0.0745737426 5.001982e-02 3.308043e-02 2.165425e-02 1.406455e-02 - [28,] 0.0076804975 2.366879e-03 7.164277e-04 2.140604e-04 6.333399e-05 - [29,] 0.0416422211 1.844892e-02 7.864587e-03 3.260072e-03 1.322968e-03 - [30,] 0.0293834515 1.761786e-02 1.065223e-02 6.479867e-03 3.959993e-03 - [31,] 0.0167643718 5.728694e-03 1.894641e-03 6.119457e-04 1.941347e-04 - [32,] 0.0005629703 1.857889e-05 4.711889e-07 9.883885e-09 1.796974e-10 - [33,] 0.0912984629 4.478754e-02 2.024516e-02 8.621054e-03 3.507999e-03 - [34,] 0.0272064068 8.622104e-03 2.550197e-03 7.174054e-04 1.942834e-04 - [35,] 0.0249487566 6.229897e-03 1.394985e-03 2.885661e-04 5.620838e-05 - [36,] 0.0047492199 3.501558e-03 2.654723e-03 2.053849e-03 1.613592e-03 - [37,] 0.0184297299 1.022149e-02 5.737544e-03 3.248766e-03 1.851735e-03 - [38,] 0.0204303410 7.690774e-03 2.815243e-03 1.009851e-03 3.567156e-04 - [39,] 0.0157573277 8.712294e-03 4.886265e-03 2.768915e-03 1.581418e-03 - [40,] 0.0222782632 1.819106e-03 9.837965e-05 3.931239e-06 1.247548e-07 - [41,] 0.0469659575 2.444861e-02 1.245402e-02 6.244902e-03 3.094191e-03 - [42,] 0.0619602658 2.473424e-02 9.056709e-03 3.113936e-03 1.020612e-03 - [43,] 0.0389013833 2.024221e-02 1.039224e-02 5.283480e-03 2.666317e-03 - [44,] 0.0179380704 1.229748e-02 8.617171e-03 6.134933e-03 4.420702e-03 - [45,] 0.0722726645 4.056157e-02 2.195877e-02 1.158016e-02 5.986351e-03 - [46,] 0.0969970096 5.353295e-02 2.769436e-02 1.366695e-02 6.506560e-03 - [47,] 0.0050333859 3.400863e-03 2.361319e-03 1.672250e-03 1.202155e-03 - [48,] 0.0058828141 3.925765e-03 2.690837e-03 1.880516e-03 1.333710e-03 - [49,] 0.0157864424 9.268886e-03 5.537016e-03 3.349369e-03 2.045392e-03 - [50,] 0.0112574559 7.346768e-03 4.910013e-03 3.338551e-03 2.299943e-03 - [51,] 0.0144690110 1.048097e-02 7.781365e-03 5.881214e-03 4.505767e-03 - [52,] 0.0527732428 3.103849e-02 1.803956e-02 1.039445e-02 5.950158e-03 - [53,] 0.0400856663 2.648773e-02 1.764753e-02 1.182838e-02 7.964204e-03 - [54,] 0.0127311556 6.464269e-03 3.323788e-03 1.724657e-03 9.010964e-04 - [55,] 0.0810476784 2.636114e-02 7.243686e-03 1.759295e-03 3.889018e-04 - [56,] 0.0186892173 8.456915e-03 3.810662e-03 1.711833e-03 7.672125e-04 - [57,] 0.0182418814 8.940763e-03 4.397069e-03 2.167838e-03 1.070793e-03 - [58,] 0.0383774154 1.681159e-02 7.105422e-03 2.926022e-03 1.181384e-03 - [59,] 0.0185170905 1.278538e-02 9.022836e-03 6.469279e-03 4.694560e-03 - [60,] 0.0208977823 1.034470e-02 5.126085e-03 2.542027e-03 1.261311e-03 - [61,] 0.0279484201 1.695351e-02 1.038968e-02 6.414549e-03 3.982675e-03 - [62,] 0.0248985165 1.256181e-02 6.326019e-03 3.181477e-03 1.598405e-03 - [63,] 0.0131904659 7.153656e-03 3.941000e-03 2.195927e-03 1.234158e-03 - [64,] 0.0201810979 7.627407e-03 2.806077e-03 1.012363e-03 3.598641e-04 - [65,] 0.0058795944 5.060864e-04 3.457104e-05 1.996717e-06 1.015613e-07 - [66,] 0.0036063821 3.197368e-04 2.357422e-05 1.520931e-06 8.870400e-08 - [67,] 0.0156984734 6.839878e-03 2.970820e-03 1.287411e-03 5.569417e-04 - [68,] 0.0149260138 8.638925e-03 5.087270e-03 3.033551e-03 1.826189e-03 - [69,] 0.0149736412 7.569641e-04 2.278390e-05 4.684466e-07 7.206513e-09 - [70,] 0.0062570718 1.895838e-03 5.665010e-04 1.675877e-04 4.920232e-05 - [71,] 0.0136794856 8.294701e-03 5.131934e-03 3.221828e-03 2.045195e-03 - [72,] 0.0021535807 9.418809e-04 4.217919e-04 1.921409e-04 8.866635e-05 - [73,] 0.0389332104 1.829341e-02 8.370727e-03 3.757320e-03 1.662076e-03 - [74,] 0.0097300722 3.255399e-03 1.071696e-03 3.487070e-04 1.124614e-04 - [75,] 0.0015088463 1.113258e-03 8.453859e-04 6.555222e-04 5.164276e-04 - [76,] 0.0513970059 1.675013e-02 4.874058e-03 1.305859e-03 3.285679e-04 - [77,] 0.0013061351 1.048698e-03 8.666331e-04 7.313244e-04 6.270334e-04 - [78,] 0.0352003329 1.510971e-02 6.266807e-03 2.535162e-03 1.006338e-03 - [79,] 0.0668651706 3.890569e-02 2.206525e-02 1.228341e-02 6.741904e-03 - [80,] 0.0143738124 7.793822e-03 4.287759e-03 2.383829e-03 1.335925e-03 - [81,] 0.0019764342 1.511525e-03 1.189634e-03 9.559950e-04 7.804896e-04 - [82,] 0.0328920593 1.661188e-02 8.301850e-03 4.117319e-03 2.030191e-03 - [83,] 0.0384658972 1.765249e-02 7.867929e-03 3.433301e-03 1.474268e-03 - [84,] 0.0200314124 1.070541e-02 5.767436e-03 3.125287e-03 1.701040e-03 - [85,] 0.0552709142 3.308335e-02 1.956788e-02 1.147401e-02 6.683868e-03 - [86,] 0.0398874770 6.383012e-03 7.737226e-04 7.650721e-05 6.477770e-06 - [87,] 0.0631139779 3.128885e-02 1.480579e-02 6.772677e-03 3.019286e-03 - [88,] 0.0009204924 7.339835e-04 6.024308e-04 5.049411e-04 4.300284e-04 - [89,] 0.0262370109 1.763186e-02 1.205324e-02 8.342470e-03 5.828737e-03 - [90,] 0.0116256478 6.296321e-03 3.469065e-03 1.935254e-03 1.089829e-03 - [91,] 0.0106340605 6.567450e-03 4.149303e-03 2.665102e-03 1.733347e-03 - [92,] 0.0246355750 7.764107e-03 2.295282e-03 6.477617e-04 1.764804e-04 - [93,] 0.0225343799 3.732233e-03 5.081698e-04 5.998346e-05 6.352435e-06 - [94,] 0.0071146699 5.347695e-03 4.131061e-03 3.255209e-03 2.604020e-03 - [95,] 0.0092404945 6.418601e-03 4.575239e-03 3.322996e-03 2.448090e-03 - [96,] 0.0211827012 1.271408e-02 7.741873e-03 4.763694e-03 2.954504e-03 - [97,] 0.0995937351 4.036533e-02 1.424045e-02 4.538726e-03 1.338793e-03 - [98,] 0.0505940345 2.766445e-02 1.484413e-02 7.856652e-03 4.115287e-03 - [99,] 0.0295530503 1.706476e-02 9.906951e-03 5.774024e-03 3.375256e-03 - [100,] 0.0257332493 1.490854e-02 8.712901e-03 5.124328e-03 3.028275e-03 - t11 t12 t13 t14 t15 - [1,] 1.518910e-03 9.739545e-04 6.278320e-04 4.065126e-04 2.642055e-04 - [2,] 2.876790e-03 2.285722e-03 1.829716e-03 1.473887e-03 1.193600e-03 - [3,] 2.837365e-05 6.882954e-06 1.639069e-06 3.843201e-07 8.893586e-08 - [4,] 2.676480e-03 1.508085e-03 8.479647e-04 4.759562e-04 2.667521e-04 - [5,] 3.074221e-04 1.083773e-04 3.768331e-05 1.295212e-05 4.408269e-06 - [6,] 2.225176e-03 9.474320e-04 3.953243e-04 1.621815e-04 6.558329e-05 - [7,] 3.010076e-03 2.220106e-03 1.648174e-03 1.230289e-03 9.226372e-04 - [8,] 8.303466e-04 4.163500e-04 2.086115e-04 1.044600e-04 5.227988e-05 - [9,] 3.280212e-03 1.760396e-03 9.385811e-04 4.976764e-04 2.626597e-04 - [10,] 3.319352e-03 1.920905e-03 1.109241e-03 6.393874e-04 3.679913e-04 - [11,] 9.200836e-04 3.845494e-04 1.588106e-04 6.493110e-05 2.632236e-05 - [12,] 6.557702e-04 2.663010e-04 1.069700e-04 4.257825e-05 1.681686e-05 - [13,] 7.169629e-08 7.810971e-09 8.318484e-10 8.692439e-11 8.936366e-12 - [14,] 1.896077e-03 1.222584e-03 7.921185e-04 5.152905e-04 3.363584e-04 - [15,] 6.896293e-04 2.291191e-04 7.407790e-05 2.341064e-05 7.256387e-06 - [16,] 6.701642e-04 2.903041e-04 1.248769e-04 5.340258e-05 2.272354e-05 - [17,] 8.074065e-04 6.438237e-04 5.175853e-04 4.189525e-04 3.410922e-04 - [18,] 9.207654e-04 5.107792e-04 2.841350e-04 1.584265e-04 8.850971e-05 - [19,] 3.224954e-03 1.666032e-03 8.529778e-04 4.334297e-04 2.188348e-04 - [20,] 4.990552e-04 2.352241e-04 1.107828e-04 5.214060e-05 2.452650e-05 - [21,] 2.197885e-03 1.133877e-03 5.816497e-04 2.969550e-04 1.509952e-04 - [22,] 2.435312e-04 1.174678e-04 5.683886e-05 2.757482e-05 1.340755e-05 - [23,] 2.719487e-03 1.576953e-03 9.137561e-04 5.291434e-04 3.062585e-04 - [24,] 2.888701e-03 1.870356e-03 1.215231e-03 7.918834e-04 5.172991e-04 - [25,] 8.955580e-05 2.627107e-05 7.587822e-06 2.163267e-06 6.099675e-07 - [26,] 2.156903e-06 3.245454e-07 4.743765e-08 6.767613e-09 9.458200e-10 - [27,] 9.079108e-03 5.831896e-03 3.730817e-03 2.378549e-03 1.512016e-03 - [28,] 1.859503e-05 5.425860e-06 1.575180e-06 4.553466e-07 1.311547e-07 - [29,] 5.279876e-04 2.079007e-04 8.096151e-05 3.123723e-05 1.195756e-05 - [30,] 2.428821e-03 1.494050e-03 9.212532e-04 5.692046e-04 3.522914e-04 - [31,] 6.072680e-05 1.878170e-05 5.754933e-06 1.749671e-06 5.284374e-07 - [32,] 2.923368e-12 4.357445e-14 9.704748e-16 3.971917e-16 0.000000e+00 - [33,] 1.377308e-03 5.253777e-04 1.956986e-04 7.145804e-05 2.565419e-05 - [34,] 5.106751e-05 1.310426e-05 3.296788e-06 8.157918e-07 1.990494e-07 - [35,] 1.044394e-05 1.868209e-06 3.239036e-07 5.470790e-08 9.037396e-09 - [36,] 1.283111e-03 1.030305e-03 8.339766e-04 6.796178e-04 5.570080e-04 - [37,] 1.060940e-03 6.104039e-04 3.524018e-04 2.040379e-04 1.184262e-04 - [38,] 1.244945e-04 4.302886e-05 1.475363e-05 5.024957e-06 1.701757e-06 - [39,] 9.087739e-04 5.248304e-04 3.043375e-04 1.770834e-04 1.033387e-04 - [40,] 3.303855e-09 7.564518e-11 1.540609e-12 2.661552e-14 0.000000e+00 - [41,] 1.518757e-03 7.398420e-04 3.581625e-04 1.724849e-04 8.269741e-05 - [42,] 3.221617e-04 9.865338e-05 2.946466e-05 8.617941e-06 2.476185e-06 - [43,] 1.337751e-03 6.680367e-04 3.323127e-04 1.647735e-04 8.147647e-05 - [44,] 3.215778e-03 2.357211e-03 1.738778e-03 1.289380e-03 9.604309e-04 - [45,] 3.046515e-03 1.530922e-03 7.613395e-04 3.753288e-04 1.836630e-04 - [46,] 3.011331e-03 1.362258e-03 6.047799e-04 2.642972e-04 1.139644e-04 - [47,] 8.744490e-04 6.421457e-04 4.752594e-04 3.540591e-04 2.652422e-04 - [48,] 9.569039e-04 6.929873e-04 5.057297e-04 3.714562e-04 2.743308e-04 - [49,] 1.258433e-03 7.789238e-04 4.845189e-04 3.026416e-04 1.897049e-04 - [50,] 1.600809e-03 1.123470e-03 7.938684e-04 5.641832e-04 4.029068e-04 - [51,] 3.488981e-03 2.724973e-03 2.143394e-03 1.695980e-03 1.348754e-03 - [52,] 3.388573e-03 1.921762e-03 1.086163e-03 6.121303e-04 3.441383e-04 - [53,] 5.381613e-03 3.646994e-03 2.477362e-03 1.686199e-03 1.149646e-03 - [54,] 4.733614e-04 2.497530e-04 1.322481e-04 7.023859e-05 3.740014e-05 - [55,] 7.983209e-05 1.543703e-05 2.841697e-06 5.019846e-07 8.562347e-08 - [56,] 3.432260e-04 1.533234e-04 6.840897e-05 3.049152e-05 1.357914e-05 - [57,] 5.296928e-04 2.623361e-04 1.300518e-04 6.452527e-05 3.203654e-05 - [58,] 4.696482e-04 1.843859e-04 7.164994e-05 2.760339e-05 1.055673e-05 - [59,] 3.439059e-03 2.538610e-03 1.885735e-03 1.408160e-03 1.056254e-03 - [60,] 6.261209e-04 3.109217e-04 1.544446e-04 7.673654e-05 3.813499e-05 - [61,] 2.483727e-03 1.554466e-03 9.757395e-04 6.139815e-04 3.871554e-04 - [62,] 8.024159e-04 4.025628e-04 2.018546e-04 1.011699e-04 5.068753e-05 - [63,] 6.983283e-04 3.972968e-04 2.270486e-04 1.302431e-04 7.495110e-05 - [64,] 1.264427e-04 4.401322e-05 1.520295e-05 5.217616e-06 1.780891e-06 - [65,] 4.676791e-09 1.988261e-10 7.918281e-12 2.977527e-13 1.001153e-14 - [66,] 4.780758e-09 2.418342e-10 1.161392e-11 5.342494e-13 2.541253e-14 - [67,] 2.406110e-04 1.038361e-04 4.477050e-05 1.928901e-05 8.305215e-06 - [68,] 1.107595e-03 6.758157e-04 4.144068e-04 2.551689e-04 1.576743e-04 - [69,] 8.831790e-11 9.003264e-13 6.182274e-15 1.464384e-16 0.000000e+00 - [70,] 1.435970e-05 4.170869e-06 1.206698e-06 3.479690e-07 1.000619e-07 - [71,] 1.309617e-03 8.444999e-04 5.477254e-04 3.569667e-04 2.336035e-04 - [72,] 4.133340e-05 1.942625e-05 9.191660e-06 4.373620e-06 2.091044e-06 - [73,] 7.268481e-04 3.149357e-04 1.354238e-04 5.786276e-05 2.458953e-05 - [74,] 3.601905e-05 1.147188e-05 3.636985e-06 1.148625e-06 3.615739e-07 - [75,] 4.119499e-04 3.319296e-04 2.696779e-04 2.206282e-04 1.815691e-04 - [76,] 7.869068e-05 1.811111e-05 4.034098e-06 8.742597e-07 1.851054e-07 - [77,] 5.443762e-04 4.774058e-04 4.221687e-04 3.759337e-04 3.367530e-04 - [78,] 3.935753e-04 1.520932e-04 5.819829e-05 2.208637e-05 8.323215e-06 - [79,] 3.659440e-03 1.968562e-03 1.051168e-03 5.578329e-04 2.944744e-04 - [80,] 7.533659e-04 4.269923e-04 2.430173e-04 1.387921e-04 7.950168e-05 - [81,] 6.451734e-04 5.386924e-04 4.535175e-04 3.844643e-04 3.278515e-04 - [82,] 9.965127e-04 4.873439e-04 2.376165e-04 1.155629e-04 5.608239e-05 - [83,] 6.251075e-04 2.623679e-04 1.092008e-04 4.513272e-05 1.854237e-05 - [84,] 9.290608e-04 5.088466e-04 2.793370e-04 1.536409e-04 8.464404e-05 - [85,] 3.873412e-03 2.235358e-03 1.285605e-03 7.372536e-04 4.217569e-04 - [86,] 4.854966e-07 3.298183e-08 2.066676e-09 1.210362e-10 6.692849e-12 - [87,] 1.319041e-03 5.669211e-04 2.404074e-04 1.008046e-04 4.186528e-05 - [88,] 3.708468e-04 3.230595e-04 2.837847e-04 2.510322e-04 2.233834e-04 - [89,] 4.102669e-03 2.905022e-03 2.067125e-03 1.476976e-03 1.059013e-03 - [90,] 6.182899e-04 3.528711e-04 2.023822e-04 1.165507e-04 6.735590e-05 - [91,] 1.138472e-03 7.537002e-04 5.022351e-04 3.365030e-04 2.265107e-04 - [92,] 4.677091e-05 1.212241e-05 3.084964e-06 7.731352e-07 1.912528e-07 - [93,] 6.178524e-07 5.611437e-08 4.817328e-09 3.945332e-10 3.104688e-11 - [94,] 2.107925e-03 1.722731e-03 1.419058e-03 1.176659e-03 9.811615e-04 - [95,] 1.823815e-03 1.371041e-03 1.038357e-03 7.913111e-04 6.062445e-04 - [96,] 1.843864e-03 1.156519e-03 7.284037e-04 4.603580e-04 2.918086e-04 - [97,] 3.715541e-04 9.816664e-05 2.490588e-05 6.107849e-06 1.455245e-06 - [98,] 2.138020e-03 1.103468e-03 5.664333e-04 2.894419e-04 1.473309e-04 - [99,] 1.977640e-03 1.160925e-03 6.825488e-04 4.018153e-04 2.368091e-04 - [100,] 1.796348e-03 1.068822e-03 6.375388e-04 3.810822e-04 2.281943e-04 - t16 t17 t18 t19 t20 - [1,] 1.722737e-04 1.126475e-04 7.384150e-05 4.851001e-05 3.193087e-05 - [2,] 9.710581e-04 7.931677e-04 6.501397e-04 5.345587e-04 4.407423e-04 - [3,] 2.034932e-08 4.610576e-09 1.035666e-09 2.308723e-10 5.111671e-11 - [4,] 1.493106e-04 8.348112e-05 4.662939e-05 2.602281e-05 1.451152e-05 - [5,] 1.487739e-06 4.984210e-07 1.659082e-07 5.491168e-08 1.808245e-08 - [6,] 2.619395e-05 1.034966e-05 4.050804e-06 1.572246e-06 6.057033e-07 - [7,] 6.946934e-04 5.248896e-04 3.978054e-04 3.023073e-04 2.302895e-04 - [8,] 2.615311e-05 1.307801e-05 6.537488e-06 3.266990e-06 1.632176e-06 - [9,] 1.380682e-04 7.232280e-05 3.776796e-05 1.966945e-05 1.021903e-05 - [10,] 2.115139e-04 1.214347e-04 6.964816e-05 3.991077e-05 2.285210e-05 - [11,] 1.059284e-05 4.235758e-06 1.684302e-06 6.664370e-07 2.625326e-07 - [12,] 6.597868e-06 2.573608e-06 9.987801e-07 3.858703e-07 1.484808e-07 - [13,] 9.103314e-13 9.200151e-14 1.114476e-14 2.284397e-16 3.527456e-15 - [14,] 2.202082e-04 1.445369e-04 9.508286e-05 6.267454e-05 4.138581e-05 - [15,] 2.211999e-06 6.645875e-07 1.971486e-07 5.782923e-08 1.679374e-08 - [16,] 9.627773e-06 4.064013e-06 1.709867e-06 7.173176e-07 3.001511e-07 - [17,] 2.790949e-04 2.293628e-04 1.892145e-04 1.566229e-04 1.300366e-04 - [18,] 4.953291e-05 2.776133e-05 1.557942e-05 8.753106e-06 4.922869e-06 - [19,] 1.098800e-04 5.490823e-05 2.732293e-05 1.354563e-05 6.693125e-06 - [20,] 1.153149e-05 5.419418e-06 2.546008e-06 1.195708e-06 5.613898e-07 - [21,] 7.651101e-05 3.865181e-05 1.947429e-05 9.788868e-06 4.910136e-06 - [22,] 6.531609e-06 3.187249e-06 1.557572e-06 7.621550e-07 3.733703e-07 - [23,] 1.771767e-04 1.024599e-04 5.923136e-05 3.423076e-05 1.977712e-05 - [24,] 3.386493e-04 2.221088e-04 1.459117e-04 9.599338e-05 6.323410e-05 - [25,] 1.703643e-07 4.719171e-08 1.297802e-08 3.546253e-09 9.635142e-10 - [26,] 1.298684e-10 1.755680e-11 2.342941e-12 3.123233e-13 3.965650e-14 - [27,] 9.587658e-04 6.066275e-04 3.830898e-04 2.415147e-04 1.520306e-04 - [28,] 3.765957e-08 1.078434e-08 3.080940e-09 8.783398e-10 2.499430e-10 - [29,] 4.546396e-06 1.718427e-06 6.461712e-07 2.418670e-07 9.016438e-08 - [30,] 2.183616e-04 1.355213e-04 8.420310e-05 5.236985e-05 3.260020e-05 - [31,] 1.586921e-07 4.742047e-08 1.410877e-08 4.181615e-09 1.235136e-09 - [32,] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 - [33,] 9.076848e-06 3.171076e-06 1.095587e-06 3.748120e-07 1.271078e-07 - [34,] 4.798346e-08 1.144614e-08 2.705351e-09 6.342340e-10 1.476181e-10 - [35,] 1.464703e-09 2.334824e-10 3.668575e-11 5.693015e-12 8.709737e-13 - [36,] 4.587736e-04 3.794876e-04 3.150881e-04 2.624905e-04 2.193234e-04 - [37,] 6.888118e-05 4.013745e-05 2.342600e-05 1.369194e-05 8.012776e-06 - [38,] 5.735081e-07 1.924582e-07 6.434496e-08 2.144179e-08 7.124138e-09 - [39,] 6.045542e-05 3.544481e-05 2.082091e-05 1.225126e-05 7.219648e-06 - [40,] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 - [41,] 3.949773e-05 1.880217e-05 8.924343e-06 4.224965e-06 1.995588e-06 - [42,] 7.006798e-07 1.956504e-07 5.399769e-08 1.474987e-08 3.992188e-09 - [43,] 4.019282e-05 1.978656e-05 9.723148e-06 4.770336e-06 2.337072e-06 - [44,] 7.181721e-04 5.388280e-04 4.054645e-04 3.059069e-04 2.313324e-04 - [45,] 8.930100e-05 4.317950e-05 2.077689e-05 9.954265e-06 4.750810e-06 - [46,] 4.857783e-05 2.049986e-05 8.575125e-06 3.559159e-06 1.467034e-06 - [47,] 1.996615e-04 1.509244e-04 1.145031e-04 8.715336e-05 6.652838e-05 - [48,] 2.035580e-04 1.516640e-04 1.134070e-04 8.507081e-05 6.399622e-05 - [49,] 1.192743e-04 7.519036e-05 4.750990e-05 3.008131e-05 1.908109e-05 - [50,] 2.889401e-04 2.079664e-04 1.501646e-04 1.087354e-04 7.893508e-05 - [51,] 1.077291e-03 8.637270e-04 6.948043e-04 5.605641e-04 4.534466e-04 - [52,] 1.930678e-04 1.081168e-04 6.044777e-05 3.374823e-05 1.881796e-05 - [53,] 7.849684e-04 5.366502e-04 3.672941e-04 2.516313e-04 1.725428e-04 - [54,] 1.995845e-05 1.067116e-05 5.715132e-06 3.065395e-06 1.646348e-06 - [55,] 1.417143e-08 2.284889e-09 3.600324e-10 5.559374e-11 8.435088e-12 - [56,] 6.042894e-06 2.687440e-06 1.194505e-06 5.306658e-07 2.356466e-07 - [57,] 1.591553e-05 7.910841e-06 3.933893e-06 1.957032e-06 9.739337e-07 - [58,] 4.012003e-06 1.516403e-06 5.704027e-07 2.136499e-07 7.972227e-08 - [59,] 7.953515e-04 6.009058e-04 4.553366e-04 3.459318e-04 2.634252e-04 - [60,] 1.895501e-05 9.423081e-06 4.685135e-06 2.329725e-06 1.158603e-06 - [61,] 2.445668e-04 1.547355e-04 9.803467e-05 6.218673e-05 3.948996e-05 - [62,] 2.538694e-05 1.271152e-05 6.363226e-06 3.184655e-06 1.593540e-06 - [63,] 4.325090e-05 2.501789e-05 1.450165e-05 8.421515e-06 4.898708e-06 - [64,] 6.050017e-07 2.046903e-07 6.900458e-08 2.318883e-08 7.770527e-09 - [65,] 2.213463e-16 9.465745e-16 0.000000e+00 0.000000e+00 0.000000e+00 - [66,] 0.000000e+00 2.540045e-15 0.000000e+00 0.000000e+00 0.000000e+00 - [67,] 3.573993e-06 1.537268e-06 6.609426e-07 2.840647e-07 1.220471e-07 - [68,] 9.772662e-05 6.073118e-05 3.782833e-05 2.361095e-05 1.476399e-05 - [69,] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 - [70,] 2.870468e-08 8.217314e-09 2.348073e-09 6.698628e-10 1.908363e-10 - [71,] 1.534154e-04 1.010639e-04 6.675713e-05 4.420162e-05 2.932950e-05 - [72,] 1.003853e-06 4.836469e-07 2.337482e-07 1.132850e-07 5.503885e-08 - [73,] 1.040104e-05 4.381705e-06 1.839351e-06 7.696963e-07 3.211862e-07 - [74,] 1.135001e-07 3.554150e-08 1.110569e-08 3.463646e-09 1.078421e-09 - [75,] 1.501868e-04 1.247795e-04 1.040739e-04 8.710324e-05 7.312364e-05 - [76,] 3.841479e-08 7.834621e-09 1.573640e-09 3.118418e-10 6.105442e-11 - [77,] 3.031998e-04 2.742053e-04 2.489529e-04 2.268080e-04 2.072700e-04 - [78,] 3.117714e-06 1.161722e-06 4.308921e-07 1.591717e-07 5.858518e-08 - [79,] 1.547467e-04 8.099941e-05 4.225115e-05 2.197175e-05 1.139475e-05 - [80,] 4.565547e-05 2.627666e-05 1.515273e-05 8.753016e-06 5.063950e-06 - [81,] 2.809972e-04 2.419047e-04 2.090597e-04 1.812953e-04 1.576998e-04 - [82,] 2.716632e-05 1.313821e-05 6.344948e-06 3.060394e-06 1.474495e-06 - [83,] 7.578952e-06 3.083999e-06 1.250023e-06 5.049132e-07 2.033175e-07 - [84,] 4.669797e-05 2.579479e-05 1.426373e-05 7.894914e-06 4.373509e-06 - [85,] 2.407641e-04 1.371907e-04 7.804740e-05 4.433773e-05 2.515574e-05 - [86,] 3.550668e-13 1.783472e-14 6.869565e-15 2.493924e-15 0.000000e+00 - [87,] 1.724442e-05 7.052303e-06 2.866039e-06 1.158289e-06 4.657996e-07 - [88,] 1.997996e-04 1.795033e-04 1.619007e-04 1.465305e-04 1.330293e-04 - [89,] 7.616214e-04 5.491820e-04 3.969116e-04 2.874463e-04 2.085492e-04 - [90,] 3.904298e-05 2.269064e-05 1.321745e-05 7.714911e-06 4.511286e-06 - [91,] 1.530824e-04 1.038180e-04 7.062304e-05 4.817188e-05 3.293719e-05 - [92,] 4.678426e-08 1.133360e-08 2.722272e-09 6.489635e-10 1.536681e-10 - [93,] 2.361105e-12 1.746038e-13 1.196085e-14 4.676881e-15 3.237907e-15 - [94,] 8.221111e-04 6.917480e-04 5.842112e-04 4.950078e-04 4.206494e-04 - [95,] 4.665824e-04 3.605217e-04 2.795419e-04 2.174207e-04 1.695696e-04 - [96,] 1.854392e-04 1.181033e-04 7.536391e-05 4.817352e-05 3.084012e-05 - [97,] 3.382178e-07 7.692682e-08 1.716842e-08 3.767958e-09 8.147020e-10 - [98,] 7.474463e-05 3.781016e-05 1.907801e-05 9.604641e-06 4.825684e-06 - [99,] 1.396955e-04 8.247549e-05 4.872841e-05 2.880832e-05 1.704127e-05 - [100,] 1.368539e-04 8.218434e-05 4.941168e-05 2.973858e-05 1.791479e-05 - t21 t22 t23 t24 t25 - [1,] 2.105479e-05 1.390521e-05 9.196573e-06 6.090333e-06 4.038072e-06 - [2,] 3.642931e-04 3.017787e-04 2.504996e-04 2.083183e-04 1.735328e-04 - [3,] 1.124902e-11 2.462957e-12 5.404331e-13 1.180779e-13 3.053494e-14 - [4,] 8.086677e-06 4.503559e-06 2.506659e-06 1.394475e-06 7.753911e-07 - [5,] 5.927535e-09 1.935124e-09 6.294079e-10 2.040294e-10 6.593324e-11 - [6,] 2.317900e-07 8.816790e-08 3.335433e-08 1.255548e-08 4.704761e-09 - [7,] 1.758082e-04 1.344781e-04 1.030462e-04 7.908831e-05 6.079004e-05 - [8,] 8.152310e-07 4.070982e-07 2.032503e-07 1.014577e-07 5.063685e-08 - [9,] 5.297667e-06 2.741004e-06 1.415677e-06 7.299904e-07 3.758622e-07 - [10,] 1.307535e-05 7.476560e-06 4.272656e-06 2.440417e-06 1.393221e-06 - [11,] 1.030127e-07 4.027633e-08 1.569663e-08 6.099386e-09 2.363718e-09 - [12,] 5.692947e-08 2.175682e-08 8.290439e-09 3.150623e-09 1.194416e-09 - [13,] 6.413556e-16 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 - [14,] 2.737180e-05 1.812922e-05 1.202321e-05 7.983196e-06 5.306449e-06 - [15,] 4.833302e-09 1.379824e-09 3.910337e-10 1.100829e-10 3.078794e-11 - [16,] 1.253037e-07 5.220133e-08 2.170592e-08 9.010070e-09 3.734166e-09 - [17,] 1.082558e-04 9.034334e-05 7.556175e-05 6.332612e-05 5.316969e-05 - [18,] 2.771236e-06 1.561309e-06 8.802993e-07 4.966713e-07 2.804010e-07 - [19,] 3.297356e-06 1.620081e-06 7.940556e-07 3.883316e-07 1.895286e-07 - [20,] 2.635061e-07 1.236559e-07 5.801581e-08 2.721409e-08 1.276334e-08 - [21,] 2.458322e-06 1.228707e-06 6.131844e-07 3.055813e-07 1.520923e-07 - [22,] 1.830990e-07 8.987469e-08 4.415260e-08 2.170749e-08 1.067994e-08 - [23,] 1.142362e-05 6.597033e-06 3.808964e-06 2.198803e-06 1.269093e-06 - [24,] 4.170254e-05 2.753123e-05 1.819276e-05 1.203219e-05 7.964009e-06 - [25,] 2.604519e-10 7.007335e-11 1.877423e-11 5.017437e-12 1.337636e-12 - [26,] 5.995740e-15 0.000000e+00 3.448121e-15 8.620303e-16 0.000000e+00 - [27,] 9.557202e-05 6.000676e-05 3.763473e-05 2.357978e-05 1.476014e-05 - [28,] 7.099680e-11 2.014388e-11 5.704938e-12 1.611035e-12 4.543691e-13 - [29,] 3.348928e-08 1.239784e-08 4.576032e-09 1.684416e-09 6.184960e-10 - [30,] 2.030977e-05 1.266198e-05 7.899163e-06 4.930806e-06 3.079571e-06 - [31,] 3.636967e-10 1.068045e-10 3.127819e-11 9.145395e-12 2.665759e-12 - [32,] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 - [33,] 4.276786e-08 1.428844e-08 4.743104e-09 1.565317e-09 5.138262e-10 - [34,] 3.413170e-11 7.844898e-12 1.788550e-12 4.105067e-13 9.200111e-14 - [35,] 1.371903e-13 2.435374e-14 0.000000e+00 0.000000e+00 2.945279e-15 - [36,] 1.837442e-04 1.543071e-04 1.298687e-04 1.095179e-04 9.252386e-05 - [37,] 4.694564e-06 2.753298e-06 1.616274e-06 9.496091e-07 5.583558e-07 - [38,] 2.360807e-09 7.804732e-10 2.574749e-10 8.476941e-11 2.786152e-11 - [39,] 4.260276e-06 2.517030e-06 1.488738e-06 8.814242e-07 5.223370e-07 - [40,] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 - [41,] 9.406383e-07 4.425543e-07 2.078636e-07 9.748178e-08 4.565192e-08 - [42,] 1.071658e-09 2.855418e-10 7.557356e-11 1.987912e-11 5.203469e-12 - [43,] 1.143511e-06 5.588668e-07 2.728492e-07 1.330835e-07 6.485570e-08 - [44,] 1.753032e-04 1.330947e-04 1.012215e-04 7.710093e-05 5.881172e-05 - [45,] 2.259584e-06 1.071369e-06 5.065535e-07 2.388896e-07 1.123958e-07 - [46,] 6.009390e-07 2.447844e-07 9.920377e-08 4.001856e-08 1.607520e-08 - [47,] 5.091623e-05 3.905904e-05 3.002672e-05 2.312774e-05 1.784538e-05 - [48,] 4.826517e-05 3.648470e-05 2.763707e-05 2.097474e-05 1.594609e-05 - [49,] 1.212332e-05 7.714035e-06 4.915002e-06 3.135419e-06 2.002406e-06 - [50,] 5.743168e-05 4.187156e-05 3.058370e-05 2.237652e-05 1.639699e-05 - [51,] 3.676612e-04 2.987378e-04 2.432021e-04 1.983374e-04 1.620084e-04 - [52,] 1.048097e-05 5.831583e-06 3.241670e-06 1.800465e-06 9.992296e-07 - [53,] 1.184052e-04 8.131182e-05 5.587482e-05 3.841787e-05 2.642916e-05 - [54,] 8.852636e-07 4.765281e-07 2.567592e-07 1.384673e-07 7.473417e-08 - [55,] 1.261502e-12 1.802357e-13 2.759244e-14 3.217394e-15 1.070132e-14 - [56,] 1.045993e-07 4.641306e-08 2.058783e-08 9.129606e-09 4.047410e-09 - [57,] 4.848430e-07 2.414344e-07 1.202574e-07 5.991407e-08 2.985665e-08 - [58,] 2.964712e-08 1.099149e-08 4.063740e-09 1.498650e-09 5.514097e-10 - [59,] 2.010152e-04 1.536797e-04 1.176912e-04 9.027036e-05 6.933663e-05 - [60,] 5.762451e-07 2.866277e-07 1.425818e-07 7.093187e-08 3.528969e-08 - [61,] 2.510140e-05 1.596946e-05 1.016781e-05 6.478571e-06 4.130651e-06 - [62,] 7.972378e-07 3.987901e-07 1.994523e-07 9.974184e-08 4.987283e-08 - [63,] 2.853758e-06 1.664691e-06 9.722469e-07 5.684588e-07 3.327054e-07 - [64,] 2.597291e-09 8.661623e-10 2.882518e-10 9.575019e-11 3.174495e-11 - [65,] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 - [66,] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 - [67,] 5.242151e-08 2.251001e-08 9.663563e-09 4.147663e-09 1.779853e-09 - [68,] 9.247099e-06 5.800286e-06 3.643134e-06 2.291030e-06 1.442351e-06 - [69,] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 - [70,] 5.428847e-11 1.543441e-11 4.372032e-12 1.247752e-12 3.504000e-13 - [71,] 1.949851e-05 1.298516e-05 8.661074e-06 5.785149e-06 3.869214e-06 - [72,] 2.679958e-08 1.307536e-08 6.390948e-09 3.128913e-09 1.534168e-09 - [73,] 1.336912e-07 5.552197e-08 2.301097e-08 9.519035e-09 3.931054e-09 - [74,] 3.352712e-10 1.040857e-10 3.227249e-11 1.000003e-11 3.097339e-12 - [75,] 6.155671e-05 5.194815e-05 4.393810e-05 3.723933e-05 3.162103e-05 - [76,] 1.183040e-11 2.273553e-12 4.276928e-13 8.358874e-14 1.621581e-14 - [77,] 1.899389e-04 1.744911e-04 1.606622e-04 1.482339e-04 1.370246e-04 - [78,] 2.149301e-08 7.861993e-09 2.868237e-09 1.043868e-09 3.790729e-10 - [79,] 5.894974e-06 3.042987e-06 1.567650e-06 8.061329e-07 4.138477e-07 - [80,] 2.933711e-06 1.701699e-06 9.881789e-07 5.744233e-07 3.342215e-07 - [81,] 1.375522e-04 1.202759e-04 1.054057e-04 9.256300e-05 8.143715e-05 - [82,] 7.097013e-07 3.412849e-07 1.639851e-07 7.873521e-08 3.777798e-08 - [83,] 8.164526e-08 3.270419e-08 1.307054e-08 5.213030e-09 2.075240e-09 - [84,] 2.424615e-06 1.345097e-06 7.466792e-07 4.147251e-07 2.304684e-07 - [85,] 1.425628e-05 8.071041e-06 4.565083e-06 2.579881e-06 1.456848e-06 - [86,] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 - [87,] 1.864881e-07 7.436405e-08 2.954594e-08 1.170028e-08 4.619367e-09 - [88,] 1.211069e-04 1.105286e-04 1.011030e-04 9.267202e-05 8.510448e-05 - [89,] 1.515542e-04 1.102975e-04 8.037893e-05 5.864697e-05 4.283817e-05 - [90,] 2.642256e-06 1.549834e-06 9.102728e-07 5.352821e-07 3.151184e-07 - [91,] 2.256927e-05 1.549510e-05 1.065708e-05 7.341420e-06 5.064781e-06 - [92,] 3.617063e-11 8.471699e-12 1.970333e-12 4.585003e-13 1.039601e-13 - [93,] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 - [94,] 3.583966e-04 3.060779e-04 2.619562e-04 2.246318e-04 1.929689e-04 - [95,] 1.325760e-04 1.038836e-04 8.156505e-05 6.415907e-05 5.055229e-05 - [96,] 1.977052e-05 1.268984e-05 8.154174e-06 5.245010e-06 3.376893e-06 - [97,] 1.738116e-10 3.664099e-11 7.639289e-12 1.576419e-12 3.195417e-13 - [98,] 2.420228e-06 1.211853e-06 6.059070e-07 3.025389e-07 1.508778e-07 - [99,] 1.008575e-05 5.971939e-06 3.537565e-06 2.096327e-06 1.242697e-06 - [100,] 1.080098e-05 6.516866e-06 3.934671e-06 2.377091e-06 1.436904e-06 - t26 t27 t28 t29 t30 - [1,] 2.680296e-06 1.780859e-06 1.184353e-06 7.883302e-07 5.251509e-07 - [2,] 1.447803e-04 1.209645e-04 1.011998e-04 8.476832e-05 7.108562e-05 - [3,] 9.137969e-16 9.244508e-15 3.177056e-15 1.927966e-15 4.284368e-16 - [4,] 4.309659e-07 2.394372e-07 1.329781e-07 7.382780e-08 4.097526e-08 - [5,] 2.124446e-11 6.831696e-12 2.190032e-12 6.960449e-13 2.250078e-13 - [6,] 1.755617e-09 6.526070e-10 2.417281e-10 8.923998e-11 3.283882e-11 - [7,] 4.678860e-05 3.605694e-05 2.781878e-05 2.148577e-05 1.661097e-05 - [8,] 2.526867e-08 1.260775e-08 6.289793e-09 3.137497e-09 1.564884e-09 - [9,] 1.932641e-07 9.925019e-08 5.091083e-08 2.608701e-08 1.335386e-08 - [10,] 7.950291e-07 4.534905e-07 2.585765e-07 1.473865e-07 8.398167e-08 - [11,] 9.137731e-10 3.524352e-10 1.356593e-10 5.211114e-11 1.998489e-11 - [12,] 4.517937e-10 1.705321e-10 6.425017e-11 2.417014e-11 9.067772e-12 - [13,] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 - [14,] 3.530722e-06 2.351372e-06 1.567283e-06 1.045477e-06 6.979092e-07 - [15,] 8.576804e-12 2.366225e-12 6.525075e-13 1.849341e-13 4.149961e-14 - [16,] 1.545366e-09 6.386951e-10 2.636392e-10 1.087044e-10 4.477736e-11 - [17,] 4.471778e-05 3.766806e-05 3.177548e-05 2.684053e-05 2.270019e-05 - [18,] 1.583942e-07 8.952181e-08 5.062100e-08 2.863712e-08 1.620731e-08 - [19,] 9.232918e-08 4.490133e-08 2.180178e-08 1.057029e-08 5.117879e-09 - [20,] 5.985005e-09 2.806068e-09 1.315451e-09 6.165868e-10 2.889708e-10 - [21,] 7.560983e-08 3.754740e-08 1.862720e-08 9.232375e-09 4.571994e-09 - [22,] 5.257863e-09 2.590040e-09 1.276571e-09 6.295093e-10 3.105769e-10 - [23,] 7.323762e-07 4.225850e-07 2.438021e-07 1.406399e-07 8.112060e-08 - [24,] 5.275118e-06 3.496400e-06 2.318872e-06 1.538792e-06 1.021674e-06 - [25,] 3.487867e-13 9.674367e-14 3.160722e-14 0.000000e+00 0.000000e+00 - [26,] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 - [27,] 9.231529e-06 5.769235e-06 3.602881e-06 2.248493e-06 1.402373e-06 - [28,] 1.234769e-13 4.122698e-14 1.025108e-14 0.000000e+00 7.562425e-15 - [29,] 2.265814e-10 8.283656e-11 3.022216e-11 1.100604e-11 3.998972e-12 - [30,] 1.924327e-06 1.203002e-06 7.523813e-07 4.707390e-07 2.946327e-07 - [31,] 7.716173e-13 2.273629e-13 6.695857e-14 1.606044e-14 5.944387e-15 - [32,] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 - [33,] 1.678439e-10 5.458519e-11 1.767035e-11 5.694165e-12 1.836654e-12 - [34,] 1.749234e-14 1.262205e-14 0.000000e+00 2.572542e-15 2.073851e-15 - [35,] 4.908799e-16 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 - [36,] 7.829719e-05 6.635985e-05 5.632238e-05 4.786610e-05 4.072923e-05 - [37,] 3.285383e-07 1.934396e-07 1.139640e-07 6.717901e-08 3.962105e-08 - [38,] 9.146546e-12 2.993962e-12 9.822127e-13 3.172954e-13 1.065751e-13 - [39,] 3.098012e-07 1.838878e-07 1.092282e-07 6.492405e-08 3.861406e-08 - [40,] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 - [41,] 2.135182e-08 9.974603e-09 4.654581e-09 2.169817e-09 1.010547e-09 - [42,] 1.355217e-12 3.523900e-13 8.335366e-14 3.053353e-14 6.801301e-15 - [43,] 3.158097e-08 1.536680e-08 7.472166e-09 3.631087e-09 1.763487e-09 - [44,] 4.491950e-05 3.435010e-05 2.629685e-05 2.015241e-05 1.545845e-05 - [45,] 5.276757e-08 2.472423e-08 1.156330e-08 5.398882e-09 2.516741e-09 - [46,] 6.432253e-09 2.564579e-09 1.019131e-09 4.037519e-10 1.594990e-10 - [47,] 1.379185e-05 1.067501e-05 8.273969e-06 6.421189e-06 4.989235e-06 - [48,] 1.214237e-05 9.259541e-06 7.070732e-06 5.406112e-06 4.138216e-06 - [49,] 1.280128e-06 8.191524e-07 5.246318e-07 3.362757e-07 2.157063e-07 - [50,] 1.203234e-05 8.841006e-06 6.503939e-06 4.790006e-06 3.531396e-06 - [51,] 1.325283e-04 1.085596e-04 8.903713e-05 7.311026e-05 6.009717e-05 - [52,] 5.541638e-07 3.071338e-07 1.701199e-07 9.417588e-08 5.210731e-08 - [53,] 1.819061e-05 1.252588e-05 8.628821e-06 5.946527e-06 4.099513e-06 - [54,] 4.036579e-08 2.181746e-08 1.179965e-08 6.385406e-09 3.457357e-09 - [55,] 0.000000e+00 6.733019e-16 0.000000e+00 0.000000e+00 0.000000e+00 - [56,] 1.793875e-09 7.948958e-10 3.521496e-10 1.559904e-10 6.907147e-11 - [57,] 1.488129e-08 7.418590e-09 3.698930e-09 1.844588e-09 9.200007e-10 - [58,] 2.024493e-10 7.418315e-11 2.713899e-11 9.905561e-12 3.618952e-12 - [59,] 5.332686e-05 4.106299e-05 3.165460e-05 2.442698e-05 1.886766e-05 - [60,] 1.755823e-08 8.736514e-09 4.347288e-09 2.163313e-09 1.076565e-09 - [61,] 2.635249e-06 1.682163e-06 1.074333e-06 6.864650e-07 4.388250e-07 - [62,] 2.493467e-08 1.246520e-08 6.230958e-09 3.114388e-09 1.556528e-09 - [63,] 1.949051e-07 1.142766e-07 6.705544e-08 3.937570e-08 2.313757e-08 - [64,] 1.050856e-11 3.477793e-12 1.145213e-12 3.753286e-13 1.276273e-13 - [65,] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 - [66,] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 - [67,] 7.636225e-10 3.275729e-10 1.404967e-10 6.024958e-11 2.583376e-11 - [68,] 9.089833e-07 5.733907e-07 3.620130e-07 2.287435e-07 1.446437e-07 - [69,] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 - [70,] 9.862201e-14 3.052330e-14 6.140742e-15 0.000000e+00 8.248023e-15 - [71,] 2.590896e-06 1.736822e-06 1.165473e-06 7.828151e-07 5.262557e-07 - [72,] 7.532783e-10 3.703304e-10 1.822840e-10 8.981301e-11 4.429883e-11 - [73,] 1.620855e-09 6.673430e-10 2.743863e-10 1.126909e-10 4.622476e-11 - [74,] 9.540115e-13 2.921537e-13 8.931537e-14 3.161224e-14 8.245949e-15 - [75,] 2.689657e-05 2.291427e-05 1.955020e-05 1.670267e-05 1.428792e-05 - [76,] 0.000000e+00 7.727395e-15 0.000000e+00 5.773224e-16 0.000000e+00 - [77,] 1.268820e-04 1.176774e-04 1.093013e-04 1.016600e-04 9.467285e-05 - [78,] 1.373710e-10 4.969648e-11 1.794701e-11 6.473772e-12 2.329260e-12 - [79,] 2.121340e-07 1.085845e-07 5.550858e-08 2.834184e-08 1.445469e-08 - [80,] 1.946297e-07 1.134297e-07 6.615485e-08 3.860917e-08 2.254719e-08 - [81,] 7.177168e-05 6.335349e-05 5.600447e-05 4.957509e-05 4.393913e-05 - [82,] 1.811497e-08 8.681354e-09 4.158217e-09 1.990733e-09 9.526170e-10 - [83,] 8.247034e-10 3.272188e-10 1.296348e-10 5.129352e-11 2.026539e-11 - [84,] 1.281353e-07 7.127151e-08 3.965874e-08 2.207621e-08 1.229311e-08 - [85,] 8.220906e-07 4.635969e-07 2.612753e-07 1.471677e-07 8.285140e-08 - [86,] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 - [87,] 1.818716e-09 7.142262e-10 2.798164e-10 1.093917e-10 4.267857e-11 - [88,] 7.829037e-05 7.213686e-05 6.656512e-05 6.150778e-05 5.690697e-05 - [89,] 3.132266e-05 2.292420e-05 1.679215e-05 1.231027e-05 9.031374e-06 - [90,] 1.856971e-07 1.095323e-07 6.466278e-08 3.820447e-08 2.258899e-08 - [91,] 3.498868e-06 2.420107e-06 1.675871e-06 1.161742e-06 8.061365e-07 - [92,] 2.284791e-14 7.312698e-15 0.000000e+00 4.674774e-15 2.889483e-15 - [93,] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 - [94,] 1.660404e-04 1.430852e-04 1.234754e-04 1.066909e-04 9.229865e-05 - [95,] 3.989264e-05 3.152544e-05 2.494591e-05 1.976358e-05 1.567556e-05 - [96,] 2.176004e-06 1.403280e-06 9.056183e-07 5.848428e-07 3.779251e-07 - [97,] 6.540567e-14 1.303873e-14 0.000000e+00 1.325791e-15 0.000000e+00 - [98,] 7.515919e-08 3.740158e-08 1.859442e-08 9.236149e-09 4.583968e-09 - [99,] 7.369029e-07 4.371027e-07 2.593433e-07 1.539134e-07 9.136498e-08 - [100,] 8.690300e-07 5.258340e-07 3.183127e-07 1.927687e-07 1.167842e-07 - t31 t32 - [1,] 3.500945e-07 0 - [2,] 5.967478e-05 0 - [3,] 0.000000e+00 0 - [4,] 2.273500e-08 0 - [5,] 6.499066e-14 0 - [6,] 1.206002e-11 0 - [7,] 1.285407e-05 0 - [8,] 7.804348e-10 0 - [9,] 6.829467e-09 0 - [10,] 4.783872e-08 0 - [11,] 7.657229e-12 0 - [12,] 3.407409e-12 0 - [13,] 0.000000e+00 0 - [14,] 4.662071e-07 0 - [15,] 1.426990e-14 0 - [16,] 1.842246e-11 0 - [17,] 1.922076e-05 0 - [18,] 9.176213e-09 0 - [19,] 2.474814e-09 0 - [20,] 1.354184e-10 0 - [21,] 2.262288e-09 0 - [22,] 1.532910e-10 0 - [23,] 4.678519e-08 0 - [24,] 6.786713e-07 0 - [25,] 3.314056e-15 0 - [26,] 0.000000e+00 0 - [27,] 8.741463e-07 0 - [28,] 1.380259e-15 0 - [29,] 1.455300e-12 0 - [30,] 1.844716e-07 0 - [31,] 0.000000e+00 0 - [32,] 0.000000e+00 0 - [33,] 5.778572e-13 0 - [34,] 4.608557e-16 0 - [35,] 0.000000e+00 0 - [36,] 3.469601e-05 0 - [37,] 2.337920e-08 0 - [38,] 3.163427e-14 0 - [39,] 2.297924e-08 0 - [40,] 0.000000e+00 0 - [41,] 4.702173e-10 0 - [42,] 4.063257e-15 0 - [43,] 8.560006e-10 0 - [44,] 1.186841e-05 0 - [45,] 1.171477e-09 0 - [46,] 6.283597e-11 0 - [47,] 3.880916e-06 0 - [48,] 3.171130e-06 0 - [49,] 1.384632e-07 0 - [50,] 2.606015e-06 0 - [51,] 4.945002e-05 0 - [52,] 2.881690e-08 0 - [53,] 2.827140e-06 0 - [54,] 1.872918e-09 0 - [55,] 0.000000e+00 0 - [56,] 3.058647e-11 0 - [57,] 4.589137e-10 0 - [58,] 1.315765e-12 0 - [59,] 1.458657e-05 0 - [60,] 5.357674e-10 0 - [61,] 2.806369e-07 0 - [62,] 7.778752e-10 0 - [63,] 1.360449e-08 0 - [64,] 3.942709e-14 0 - [65,] 0.000000e+00 0 - [66,] 0.000000e+00 0 - [67,] 1.107086e-11 0 - [68,] 9.152811e-08 0 - [69,] 0.000000e+00 0 - [70,] 0.000000e+00 0 - [71,] 3.540701e-07 0 - [72,] 2.187166e-11 0 - [73,] 1.894059e-11 0 - [74,] 0.000000e+00 0 - [75,] 1.223666e-05 0 - [76,] 0.000000e+00 0 - [77,] 8.827027e-05 0 - [78,] 8.349922e-13 0 - [79,] 7.364364e-09 0 - [80,] 1.317495e-08 0 - [81,] 3.898966e-05 0 - [82,] 4.556595e-10 0 - [83,] 7.998964e-12 0 - [84,] 6.847621e-09 0 - [85,] 4.662040e-08 0 - [86,] 0.000000e+00 0 - [87,] 1.661972e-11 0 - [88,] 5.271274e-05 0 - [89,] 6.630443e-06 0 - [90,] 1.336536e-08 0 - [91,] 5.598975e-07 0 - [92,] 8.890718e-16 0 - [93,] 0.000000e+00 0 - [94,] 7.993728e-05 0 - [95,] 1.244625e-05 0 - [96,] 2.443578e-07 0 - [97,] 7.075648e-16 0 - [98,] 2.273309e-09 0 - [99,] 5.424730e-08 0 - [100,] 7.077584e-08 0 - - $SI.Moments - Mean Std - 1 1.973546 1.5918217 - 2 1.764371 2.2976404 - 3 2.929508 1.0897658 - 4 3.087429 1.8691624 - 5 3.175781 1.3473058 - 6 4.111781 1.6949216 - 7 1.978759 2.0624655 - 8 2.555066 1.4919049 - 9 3.543836 1.9106106 - 10 3.193901 1.9594887 - 11 3.382136 1.5372825 - 12 3.219826 1.4719356 - 13 2.444204 0.7646238 - 14 2.121850 1.7089708 - 15 3.958680 1.4486061 - 16 2.987672 1.4730975 - 17 1.222940 1.2925027 - 18 2.205710 1.4703433 - 19 3.700025 1.8815879 - 20 2.435476 1.3733192 - 21 3.296963 1.7783316 - 22 1.911244 1.1462524 - 23 2.964582 1.8842665 - 24 2.487654 1.9405539 - 25 2.998106 1.1939868 - 26 2.941120 0.9353185 - 27 4.033024 2.4901999 - 28 2.232779 0.9779327 - 29 3.169720 1.4324727 - 30 2.560760 1.8448697 - 31 2.628002 1.1283634 - 32 2.788792 0.5975207 - 33 4.065555 1.5766267 - 34 3.075510 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14378, 14379, 14380, 14381, 14382, 14383, 14384, 14385, 14386, 14387, 14388, 14389, 14390, 14391, 14392] + }, + { + "type": "double", + "attributes": {}, + "value": [1, 1, 0, 2, 5, 3, 3, 3, 6, 2, 5, 9, 13, 12, 13, 11, 12, 6, 6, 6, 3, 1, 0, 2, 0, 0, 0, 0, 2, 0, 2, 0] + }, + { + "type": "double", + "attributes": {}, + "value": [0, 1, 0, 2, 5, 3, 3, 3, 6, 2, 5, 9, 13, 12, 13, 11, 12, 6, 6, 6, 3, 1, 0, 2, 0, 0, 0, 0, 2, 0, 2, 0] + }, + { + "type": "double", + "attributes": {}, + "value": [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] + } + ] + } diff --git a/tests/testthat/test-estimate_R.R b/tests/testthat/test-estimate_R.R index 8ca71faf..2a0f7c85 100644 --- a/tests/testthat/test-estimate_R.R +++ b/tests/testthat/test-estimate_R.R @@ -17,7 +17,7 @@ test_that("Example 1 matches saved output", { config = list(t_start = 2:26, t_end = 8:32, si_distr = Flu2009$si_distr, seed = 1)) - expect_snapshot(out) + epi_snapshot_value(out, n = NULL) }) test_that("Example 2 matches saved output", { @@ -33,7 +33,7 @@ test_that("Example 2 matches saved output", { mean_si = 2.6, std_si = 1.5, seed = 1)) - expect_snapshot(out) + epi_snapshot_value(out, n = NULL) }) test_that("Example 3 matches saved output", { @@ -43,7 +43,7 @@ test_that("Example 3 matches saved output", { mean_si = 2.6, std_si = 1.5, seed = 1)) - expect_snapshot(out) + epi_snapshot_value(out, n = NULL) }) test_that("Example 4 matches saved output", { @@ -61,5 +61,5 @@ test_that("Example 4 matches saved output", { n1 = 100, n2 = 100, seed = 1)) - expect_snapshot(out) + epi_snapshot_value(out, n = NULL) }) From a2f1e8fac68bade1f6cfb4ca4ab2767e95197987 Mon Sep 17 00:00:00 2001 From: Steffi LaZerte Date: Thu, 28 May 2026 15:48:28 -0500 Subject: [PATCH 06/31] Convert serialized snapshots to snapshot values - back-compatibility (fixes #239) --- tests/testthat/_snaps/back-compatibility.md | 782 +++++++++++++------- tests/testthat/test-back-compatibility.R | 8 +- 2 files 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0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] + }, + { + "type": "list", + "attributes": { + "names": { + "type": "character", + "attributes": {}, + "value": ["Mean", "Std"] + }, + "class": { + "type": "character", + "attributes": {}, + "value": ["data.frame"] + }, + "row.names": { + "type": "integer", + "attributes": {}, + "value": [1] + } + }, + "value": [ + { + "type": "double", + "attributes": {}, + "value": [2.596] + }, + { + "type": "double", + "attributes": {}, + "value": [1.53453055] + } + ] + }, + { + "type": "integer", + "attributes": {}, + "value": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32] + }, + { + "type": "double", + "attributes": {}, + "value": [1, 1, 0, 2, 5, 3, 3, 3, 6, 2, 5, 9, 13, 12, 13, 11, 12, 6, 6, 6, 3, 1, 0, 2, 0, 0, 0, 0, 2, 0, 2, 0] + }, + { + "type": "double", + "attributes": {}, + "value": [0, 1, 0, 2, 5, 3, 3, 3, 6, 2, 5, 9, 13, 12, 13, 11, 12, 6, 6, 6, 3, 1, 0, 2, 0, 0, 0, 0, 2, 0, 2, 0] + }, + { + "type": "double", + "attributes": {}, + "value": [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] + } + ] + } diff --git a/tests/testthat/test-back-compatibility.R b/tests/testthat/test-back-compatibility.R index c4f98543..9e935541 100644 --- a/tests/testthat/test-back-compatibility.R +++ b/tests/testthat/test-back-compatibility.R @@ -6,21 +6,23 @@ test_that("These things still calculate the same after refactoring", { method = "non_parametric_si", config = make_config(list(si_distr = Flu2009$si_distr)) ) - expect_snapshot_value(x1, style = "serialize") + expect_snapshot_value(x1, style = "json2") + + x2 <- estimate_R( Flu2009$incidence$I, method = "non_parametric_si", config = make_config(list(si_distr = Flu2009$si_distr)) ) - expect_snapshot_value(x2, style = "serialize") + expect_snapshot_value(x2, style = "json2") x3 <- estimate_R( data.frame(Flu2009$incidence$I), method = "non_parametric_si", config = make_config(list(si_distr = Flu2009$si_distr)) ) - expect_snapshot_value(x3, style = "serialize") + expect_snapshot_value(x3, style = "json2") }) \ No newline at end of file From b4226474acbf9b0ae6732584d66b61a1e8157cf1 Mon Sep 17 00:00:00 2001 From: Steffi LaZerte Date: Thu, 28 May 2026 15:57:56 -0500 Subject: [PATCH 07/31] Add snapshot tests - walinga_teunis() --- tests/testthat/_snaps/wallinga_teunis.md | 115 +++++++++++++++++++++++ tests/testthat/test-wallinga_teunis.R | 22 ++--- 2 files changed, 125 insertions(+), 12 deletions(-) create mode 100644 tests/testthat/_snaps/wallinga_teunis.md diff --git a/tests/testthat/_snaps/wallinga_teunis.md b/tests/testthat/_snaps/wallinga_teunis.md new file mode 100644 index 00000000..d9892ad3 --- /dev/null +++ b/tests/testthat/_snaps/wallinga_teunis.md @@ -0,0 +1,115 @@ +# Example 1 matches saved output + + { + "type": "list", + "attributes": { + "names": { + "type": "character", + "attributes": {}, + "value": ["R", "method", "si_distr", "SI.Moments", "dates", "I", "I_local", "I_imported"] + } + }, + "value": [ + { + "type": "list", + "attributes": { + "names": { + "type": "character", + "attributes": {}, + "value": ["t_start", "t_end", "Mean(R)", "Std(R)", "Quantile.0.025(R)", "Quantile.0.975(R)"] + } + }, + "value": [ + { + "type": "double", + "attributes": {}, + "value": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26] + }, + { + "type": "double", + "attributes": {}, + "value": [8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32] + }, + { + "type": "double", + "attributes": {}, + "value": [1.53469283, 1.45645324, 1.49659674, 1.51124423, 1.57067283, 1.46870723, 1.35093393, 1.2336289, 1.11269105, 0.99805824, 0.89598538, 0.77478512, 0.6519836, 0.56694111, 0.4935998, 0.44405753, 0.41514512, 0.37801074, 0.35816826, 0.43534608, 0.58003403, 0.78957957, 0.78957957, 0.4717477, 0.4717477] + }, + { + "type": "double", + "attributes": {}, + "value": [0.14111643, 0.12551463, 0.11762242, 0.11674792, 0.08396713, 0.06676998, 0.07022297, 0.05456902, 0.05424254, 0.03915201, 0.04457781, 0.03690389, 0.04438971, 0.04578859, 0.0477754, 0.08265465, 0.08069374, 0.09639509, 0.10886187, 0.19389784, 0.3086067, 0.1953542, 0.1953542, 0.09258201, 0.09258201] + }, + { + "type": "double", + "attributes": {}, + "value": [1.23529412, 1.2375, 1.259375, 1.33333333, 1.41935484, 1.36585366, 1.24, 1.12041667, 1.02230769, 0.93333333, 0.81875, 0.71232877, 0.57575758, 0.48157895, 0.405, 0.27132353, 0.259375, 0.17916667, 0.10208333, 0.16666667, 0, 0.30625, 0.30625, 0.25, 0.25] + }, + { + "type": "double", + "attributes": {}, + "value": [1.82352941, 1.75227273, 1.73125, 1.73240741, 1.73467742, 1.63658537, 1.4955, 1.31291667, 1.2, 1.077, 0.97072368, 0.83561644, 0.72386364, 0.64912281, 0.59, 0.61102941, 0.54166667, 0.55555556, 0.5, 0.83333333, 1, 1, 1, 0.5, 0.5] + } + ] + }, + { + "type": "character", + "attributes": {}, + "value": ["non_parametric_si"] + }, + { + "type": "double", + "attributes": {}, + "value": [0, 0.233, 0.359, 0.198, 0.103, 0.053, 0.027, 0.014, 0.007, 0.003, 0.002, 0.001, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] + }, + { + "type": "list", + "attributes": { + "names": { + "type": "character", + "attributes": {}, + "value": ["Mean", "Std"] + } + }, + "value": [ + { + "type": "double", + "attributes": {}, + "value": [2.596] + }, + { + "type": "double", + "attributes": {}, + "value": [1.53453055] + } + ] + }, + { + "type": "double", + "attributes": { + "class": { + "type": "character", + "attributes": {}, + "value": ["Date"] + } + }, + "value": [14361, 14362, 14363, 14364, 14365, 14366, 14367, 14368, 14369, 14370, 14371, 14372, 14373, 14374, 14375, 14376, 14377, 14378, 14379, 14380, 14381, 14382, 14383, 14384, 14385, 14386, 14387, 14388, 14389, 14390, 14391, 14392] + }, + { + "type": "double", + "attributes": {}, + "value": [1, 1, 0, 2, 5, 3, 3, 3, 6, 2, 5, 9, 13, 12, 13, 11, 12, 6, 6, 6, 3, 1, 0, 2, 0, 0, 0, 0, 2, 0, 2, 0] + }, + { + "type": "double", + "attributes": {}, + "value": [0, 1, 0, 2, 5, 3, 3, 3, 6, 2, 5, 9, 13, 12, 13, 11, 12, 6, 6, 6, 3, 1, 0, 2, 0, 0, 0, 0, 2, 0, 2, 0] + }, + { + "type": "double", + "attributes": {}, + "value": [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] + } + ] + } + diff --git a/tests/testthat/test-wallinga_teunis.R b/tests/testthat/test-wallinga_teunis.R index 106325a3..6014e887 100644 --- a/tests/testthat/test-wallinga_teunis.R +++ b/tests/testthat/test-wallinga_teunis.R @@ -295,15 +295,13 @@ test_that( } ) - -## data("Flu2009") - -## test_that("Example 1 matches saved output", { -## out <- wallinga_teunis(Flu2009$incidence, method = "non_parametric_si", -## config = list(t_start = 2:26, t_end = 8:32, -## si_distr = Flu2009$si_distr, -## seed = 1, -## n_sim = 50)) -## ## TODO: save output (with fixed seed) into am rda as we did for estimate_T -## #expect_equal_to_reference(out, "../expected_output/Example1.rda", update = FALSE) -## }) +test_that("Example 1 matches saved output", { + data("Flu2009") + + out <- wallinga_teunis(Flu2009$incidence, method = "non_parametric_si", + config = list(t_start = 2:26, t_end = 8:32, + si_distr = Flu2009$si_distr, + seed = 1, + n_sim = 50)) + epi_snapshot_value(out, n = NULL) +}) \ No newline at end of file From 098cfe80e80e1a95d73ce94069f6476bcd47f9bd Mon Sep 17 00:00:00 2001 From: Steffi LaZerte Date: Thu, 28 May 2026 15:58:45 -0500 Subject: [PATCH 08/31] Update epi_shapshot_value() to recurse infinitely as needed --- R/test_utils.R | 20 +++++++++++++------- 1 file changed, 13 insertions(+), 7 deletions(-) diff --git a/R/test_utils.R b/R/test_utils.R index cba48f97..bb24861c 100644 --- a/R/test_utils.R +++ b/R/test_utils.R @@ -38,9 +38,9 @@ epi_expect_doppelganger <- function(name, code, variant) { #' Custom snapshot utility function #' #' Creates mini snapshots by sampling `x` down to `n`. If `x` is a list, the -#' sampling applies to all sub-items (which may or may not be appropriate, -#' depending on the situation). Also rounds to 8 digits to avoid mismatches in -#' re-loading values with long trailing decimals. +#' function recurses down the list sampling applies to all sub-items (which may +#' or may not be appropriate, depending on the situation). Also rounds to 8 +#' digits to avoid mismatches in re-loading values with long trailing decimals. #' #' @param x Vector or list of vectors to take a snapshot of. #' @param style Snapshot style (see `?testthat::snapshot_value()`) @@ -51,15 +51,21 @@ epi_snapshot_value <- function(x, style = "json2", n = 5) { set.seed(1) # TODO: could use withr::with_seed() sample_x <- function(xx) { - # take random sample of 5 points or all - nn <- if(length(xx) < n) length(xx) else n - xx <- sample(xx, size = nn) + # Recurse if this is still a list + if(is.list(xx)) return(lapply(xx, sample_x)) + + # Take random sample of points or max number + if(!is.null(n)) { + nn <- if(length(xx) < n) length(xx) else n + xx <- sample(xx, size = nn) + } + # Round to avoid mismatches from truncated records if(is.numeric(xx)) xx <- round(xx, digits = 8) xx } - if(is.list(x)) x <- lapply(x, sample_x) else x <- sample_x(x) + x <- sample_x(x) testthat::expect_snapshot_value(x, style = style) } From 3f97e689b76cf93c22d4ca1c9cd7fc46c3ce93ab Mon Sep 17 00:00:00 2001 From: Steffi LaZerte Date: Tue, 2 Jun 2026 11:50:32 -0500 Subject: [PATCH 09/31] Add gibs draws specific tests --- tests/testthat/_snaps/gibs_draws.md | 97 +++++++++++++++++++++++ tests/testthat/test-gibs_draws.R | 118 ++++++++++++++++++++++++++++ 2 files changed, 215 insertions(+) create mode 100644 tests/testthat/_snaps/gibs_draws.md create mode 100644 tests/testthat/test-gibs_draws.R diff --git a/tests/testthat/_snaps/gibs_draws.md b/tests/testthat/_snaps/gibs_draws.md new file mode 100644 index 00000000..588785be --- /dev/null +++ b/tests/testthat/_snaps/gibs_draws.md @@ -0,0 +1,97 @@ +# process_I_multivariant() + + { + "type": "list", + "attributes": { + "names": { + "type": "character", + "attributes": {}, + "value": ["local", "imported"] + } + }, + "value": [ + { + "type": "double", + "attributes": {}, + "value": [10, 10, 10, 10, 10] + }, + { + "type": "double", + "attributes": {}, + "value": [0, 0, 0, 0, 0] + } + ] + } + +# compute_lambda() + + { + "type": "double", + "attributes": {}, + "value": [10, 10, 10, 10, 10] + } + +# draw_R() + + { + "type": "double", + "attributes": {}, + "value": [1.34883043, 0.84282641, 0.79784422, 1.38220267, 0.78447105] + } + +# estimate_advantage() + + { + "type": "list", + "attributes": { + "names": { + "type": "character", + "attributes": {}, + "value": ["epsilon", "R", "convergence", "diag"] + } + }, + "value": [ + { + "type": "double", + "attributes": {}, + "value": [1.00471817, 1.03107137, 1.02035086, 1.00506093, 1.00732389] + }, + { + "type": "double", + "attributes": {}, + "value": [0.95435841, 1.31424638, 0.64631375, 1.02244546, 1.14068966] + }, + { + "type": "logical", + "attributes": {}, + "value": [false] + }, + { + "type": "list", + "attributes": {}, + "value": [ + { + "type": "list", + "attributes": { + "names": { + "type": "character", + "attributes": {}, + "value": ["psrf", "mpsrf"] + } + }, + "value": [ + { + "type": "double", + "attributes": {}, + "value": [1.02097152, 1.11993594] + }, + { + "type": "NULL" + } + ] + } + ] + } + ] + } + diff --git a/tests/testthat/test-gibs_draws.R b/tests/testthat/test-gibs_draws.R new file mode 100644 index 00000000..11d59e36 --- /dev/null +++ b/tests/testthat/test-gibs_draws.R @@ -0,0 +1,118 @@ +# Shared setup ---------------------------------------------------------------- +n_v <- 2 # 2 variants +n_loc <- 3 # 3 locations +time_stps <- 100 # 100 time steps +priors <- default_priors() + +w_v <- c(0, 0.2, 0.5, 0.3) +si_distr <- cbind(w_v, w_v) +epsilon <- 1 + +incid0 <- array(10, dim = c(time_stps, n_loc, n_v)) +incid_mv0 <- process_I_multivariant(incid0) +lambda0 <- compute_lambda(incid_mv0, si_distr) + +#R <- matrix(1, nrow = T, ncol = n_loc) +#R[1, ] <- NA + +test_that("defaults", { + expect_silent(default_priors()) |> + expect_equal( + list(epsilon = list(shape = 1, scale = 1), R = list(shape = 0.04, scale = 25)) + ) + expect_silent(default_mcmc_controls()) |> + expect_equal( + list(n_iter = 1100, burnin = 10, thin = 10) + ) +}) + + +test_that("process_I_multivariant()", { + incid <- array(10, dim = c(100, 1, 2)) + + epi_snapshot_value(process_I_multivariant(incid)) +}) + +test_that("get_shape_R_flat()", { + incid <- array(10, dim = c(100, 3, 2)) + + expect_silent(get_shape_R_flat(incid, default_priors())) |> + expect_all_equal(20.04) +}) + +test_that("compute_lambda()", { + w_v <- c(0, 0.2, 0.5, 0.3) + si_distr <- cbind(w_v, w_v) + incid <- array(10, dim = c(100, 3, 2)) + incid_mv <- process_I_multivariant(incid) + + expect_silent(lambda <- compute_lambda(incid_mv, si_distr)) + epi_snapshot_value(lambda) +}) + +test_that("Epsilon: get_shape_epsilon(), draw_epsilon()", { + incid <- array(10, dim = c(100, 4, 3)) + incid_mv <- process_I_multivariant(incid) + w_v <- c(0, 0.2, 0.5, 0.3) + si_distr <- cbind(w_v, w_v, w_v) + lambda <- compute_lambda(incid_mv, si_distr) + + expect_silent(get_shape_epsilon(incid_mv$local, lambda, default_priors())) |> + expect_equal(c(3961, 3961)) + + R <- matrix(1, nrow = 100, ncol = 4) + R[1, ] <- NA # no estimates of R on first time step + + expect_silent( + draw_epsilon(R, incid_mv$local, lambda, default_priors(), seed = 1) + ) |> + expect_equal(c(1.001066, 1.032584), tolerance = 0.000001) +}) + +test_that("draw_R()", { + w_v <- c(0, 0.2, 0.5, 0.3) + si_distr <- cbind(w_v, w_v) + incid <- array(10, dim = c(100, 3, 2)) + incid_mv <- process_I_multivariant(incid) + lambda <- compute_lambda(incid_mv, si_distr) + + expect_silent(result <- draw_R(1, incid_mv$local, lambda, default_priors(), + seed = 1, t_min = 2L)) + epi_snapshot_value(result) +}) + +test_that("first_nonzero_incid()", { + incid <- array(10, dim = c(100, 3, 2)) + incid_mv <- process_I_multivariant(incid) + + expect_silent(first_nonzero_incid(incid_mv$local)) |> + expect_equal(2) +}) + +test_that("compute_si_cutoff snapshot", { + w_v <- c(0, 0.2, 0.5, 0.3) + si_distr <- cbind(w_v, w_v) + + expect_silent(compute_si_cutoff(si_distr)) |> + expect_equal(4) +}) + +test_that("compute_t_min snapshot", { + incid <- array(10, dim = c(100, 3, 2)) + incid_mv <- process_I_multivariant(incid) + + expect_silent(compute_t_min(incid_mv$local, si_distr)) |> + expect_equal(6) +}) + +test_that("estimate_advantage()", { + incid <- array(10, dim = c(100, 3, 2)) + w_v <- c(0, 0.2, 0.5, 0.3) + si_distr <- cbind(w_v, w_v) + + expect_message( + est <- estimate_advantage(incid, si_distr, default_priors(), seed = 1) + ) + epi_snapshot_value(est) +}) + From 0ee26d732f14a96f0b9d78c5f7ca24227021f7bc Mon Sep 17 00:00:00 2001 From: Steffi LaZerte Date: Tue, 2 Jun 2026 14:33:18 -0500 Subject: [PATCH 10/31] Fix snapshots - don't randomize if not samping --- R/test_utils.R | 9 ++++----- 1 file changed, 4 insertions(+), 5 deletions(-) diff --git a/R/test_utils.R b/R/test_utils.R index bb24861c..a7711c4d 100644 --- a/R/test_utils.R +++ b/R/test_utils.R @@ -48,16 +48,15 @@ epi_expect_doppelganger <- function(name, code, variant) { #' #' @noRd epi_snapshot_value <- function(x, style = "json2", n = 5) { - set.seed(1) # TODO: could use withr::with_seed() sample_x <- function(xx) { # Recurse if this is still a list if(is.list(xx)) return(lapply(xx, sample_x)) - # Take random sample of points or max number - if(!is.null(n)) { - nn <- if(length(xx) < n) length(xx) else n - xx <- sample(xx, size = nn) + # Take random sample of points if less than n + if(!is.null(n) && length(xx) > n) { + set.seed(1) # TODO: could use withr::with_seed() + xx <- sample(xx, size = n) } # Round to avoid mismatches from truncated records From f00e031a6373fd6b8af3e2415205e0b29e78449b Mon Sep 17 00:00:00 2001 From: Steffi LaZerte Date: Tue, 2 Jun 2026 14:33:33 -0500 Subject: [PATCH 11/31] Check for class removed with custom snapshot --- tests/testthat/test-gibs_draws.R | 2 ++ tests/testthat/test-samplers.R | 11 +++++++++++ 2 files changed, 13 insertions(+) diff --git a/tests/testthat/test-gibs_draws.R b/tests/testthat/test-gibs_draws.R index 11d59e36..92f9c059 100644 --- a/tests/testthat/test-gibs_draws.R +++ b/tests/testthat/test-gibs_draws.R @@ -113,6 +113,8 @@ test_that("estimate_advantage()", { expect_message( est <- estimate_advantage(incid, si_distr, default_priors(), seed = 1) ) + + expect_s3_class(est$diag[[1]], "gelman.diag") epi_snapshot_value(est) }) diff --git a/tests/testthat/test-samplers.R b/tests/testthat/test-samplers.R index 54b2f434..e6463055 100644 --- a/tests/testthat/test-samplers.R +++ b/tests/testthat/test-samplers.R @@ -336,6 +336,7 @@ test_that("estimate_advantage produces expected results (2 variants 3 locations) mean_R <- rowMeans(x$R, dims = 2) expect_lt(max(abs(mean_R[-c(1, 2, 3), ] - 1)), 0.1) + expect_s3_class(x$diag[[1]], "gelman.diag") epi_snapshot_value(x) }) @@ -365,6 +366,7 @@ test_that("estimate_advantage produces expected results (2 variants 1 location)" mean_R <- rowMeans(x$R, dims = 2) expect_lt(max(abs(mean_R[-c(1, 2, 3), ] - 1)), 0.1) + expect_s3_class(x$diag[[1]], "gelman.diag") epi_snapshot_value(x) }) @@ -399,6 +401,7 @@ test_that("estimate_advantage produces expected results (>2 variants 4 locs)", { mean_R <- rowMeans(x$R, dims = 2) expect_lt(max(abs(mean_R[-c(1, 2, 3), ] - 1)), 0.1) + expect_s3_class(x$diag[[1]], "gelman.diag") epi_snapshot_value(x) }) @@ -430,6 +433,7 @@ test_that("estimate_advantage produces expected results (>2 variants 1 loc)", { mean_R <- rowMeans(x$R, dims = 2) expect_lt(max(abs(mean_R[-c(1, 2, 3), ] - 1)), 0.1) + expect_s3_class(x$diag[[1]], "gelman.diag") epi_snapshot_value(x) }) @@ -525,6 +529,7 @@ test_that("estimate_advantage produces expected results (>2var, 1loc, imports)", mean_R <- rowMeans(x$R, dims = 2) expect_lt(max(abs(mean_R[-c(1, 2, 3), ] - 1)), 0.1) + expect_s3_class(x$diag[[1]], "gelman.diag") epi_snapshot_value(x) }) @@ -565,6 +570,7 @@ test_that("estimate_advantage produces expected results (>2var, 4loc, imports)", mean_R <- rowMeans(x$R, dims = 2) expect_lt(max(abs(mean_R[-c(1, 2, 3), ] - 1)), 0.1) + expect_s3_class(x$diag[[1]], "gelman.diag") epi_snapshot_value(x) }) @@ -600,6 +606,7 @@ test_that("estimate_advantage produces expected results (2var, 1loc, imports)", mean_R <- rowMeans(x$R, dims = 2) expect_lt(max(abs(mean_R[-c(1, 2, 3), ] - 1)), 0.1) + expect_s3_class(x$diag[[1]], "gelman.diag") epi_snapshot_value(x) }) @@ -638,6 +645,7 @@ test_that("estimate_advantage produces expected results (2var, 4loc, imports)", mean_R <- rowMeans(x$R, dims = 2) expect_lt(max(abs(mean_R[-c(1, 2, 3), ] - 1)), 0.1) + expect_s3_class(x$diag[[1]], "gelman.diag") epi_snapshot_value(x) }) @@ -702,6 +710,7 @@ test_that("estimate_advantage produces expected results (2 var, 2 loc, R_loc1 = expect_equal(mean(x$R[,1,], na.rm = TRUE), 1.1, tolerance = 0.5) expect_equal(mean(x$R[,2,], na.rm = TRUE), 1.5, tolerance = 0.5) + expect_s3_class(x$diag[[1]], "gelman.diag") epi_snapshot_value(x) }) @@ -987,6 +996,7 @@ test_that("estimate_advantage uses the correct t_min", { expect_true(all(is.na(x$R[seq(1, t_min - 1, 1), , ]))) expect_false(anyNA(x$R[seq(t_min, dim(x$R)[1]), , ])) + expect_s3_class(x$diag[[1]], "gelman.diag") epi_snapshot_value(x) }) @@ -1015,6 +1025,7 @@ test_that("estimate_advantage convergence checks work with >2 variants", { expect_length(x$convergence, 2) expect_length(x$diag, 2) + expect_s3_class(x$diag[[1]], "gelman.diag") epi_snapshot_value(x) }) From 859c7e40a17d0307c5dc4acf6eba0c3e96d889d7 Mon Sep 17 00:00:00 2001 From: Steffi LaZerte Date: Tue, 2 Jun 2026 14:35:44 -0500 Subject: [PATCH 12/31] Update snapshots --- tests/testthat/_snaps/gibs_draws.md | 65 +- tests/testthat/_snaps/samplers.md | 8110 +++++++++++++-------------- 2 files changed, 4037 insertions(+), 4138 deletions(-) diff --git a/tests/testthat/_snaps/gibs_draws.md b/tests/testthat/_snaps/gibs_draws.md index 588785be..31dd4da3 100644 --- a/tests/testthat/_snaps/gibs_draws.md +++ b/tests/testthat/_snaps/gibs_draws.md @@ -1,44 +1,3 @@ -# process_I_multivariant() - - { - "type": "list", - "attributes": { - "names": { - "type": "character", - "attributes": {}, - "value": ["local", "imported"] - } - }, - "value": [ - { - "type": "double", - "attributes": {}, - "value": [10, 10, 10, 10, 10] - }, - { - "type": "double", - "attributes": {}, - "value": [0, 0, 0, 0, 0] - } - ] - } - -# compute_lambda() - - { - "type": "double", - "attributes": {}, - "value": [10, 10, 10, 10, 10] - } - -# draw_R() - - { - "type": "double", - "attributes": {}, - "value": [1.34883043, 0.84282641, 0.79784422, 1.38220267, 0.78447105] - } - # estimate_advantage() { @@ -59,7 +18,7 @@ { "type": "double", "attributes": {}, - "value": [0.95435841, 1.31424638, 0.64631375, 1.02244546, 1.14068966] + "value": [0.91800364, 0.85016542, 0.71860597, 1.07449868, 1.30864555] }, { "type": "logical", @@ -82,7 +41,27 @@ "value": [ { "type": "double", - "attributes": {}, + "attributes": { + "dim": { + "type": "integer", + "attributes": {}, + "value": [1, 2] + }, + "dimnames": { + "type": "list", + "attributes": {}, + "value": [ + { + "type": "NULL" + }, + { + "type": "character", + "attributes": {}, + "value": ["Point est.", "Upper C.I."] + } + ] + } + }, "value": [1.02097152, 1.11993594] }, { diff --git a/tests/testthat/_snaps/samplers.md b/tests/testthat/_snaps/samplers.md index fa426b1d..15e263b9 100644 --- a/tests/testthat/_snaps/samplers.md +++ b/tests/testthat/_snaps/samplers.md @@ -52,4997 +52,4997 @@ { "type": "double", "attributes": {}, - "value": [1.18978029, 0.72531849, 0.94513563, 0.8134282, 0.67430978] + "value": [0.93882838, 1.28206494, 1.20229884, 1.06496217, 1.01970474] }, { "type": "double", "attributes": {}, - "value": [0.96529267, 0.83446999, 0.95757407, 0.87502567, 1.23632585] + "value": [1.51780022, 0.96531844, 1.10928708, 0.91372191, 1.12348027] }, { "type": "double", "attributes": {}, - "value": [1.34807372, 1.04881691, 0.85322226, 0.90004841, 0.86758916] + "value": [0.70998505, 0.97325563, 0.73939673, 0.7963231, 0.75603737] }, { "type": "double", "attributes": {}, - "value": [0.93898419, 0.87526832, 0.87859939, 0.79146857, 0.98123812] + "value": [1.12013827, 0.71479584, 1.02251284, 1.08188688, 0.87584597] }, { "type": "double", "attributes": {}, - "value": [0.82383585, 1.19012547, 0.77182392, 1.20593719, 1.36078845] + "value": [1.16111275, 1.27466446, 1.05636154, 1.01782201, 1.07040421] }, { "type": "double", "attributes": {}, - "value": [0.69958733, 0.97504043, 1.21415603, 0.95178713, 0.8807506] + "value": [1.02100017, 1.01730698, 0.85741344, 1.03036101, 1.2554566] }, { "type": "double", "attributes": {}, - "value": [0.70114876, 0.93998129, 1.12884743, 1.06330207, 0.80537808] + "value": [0.88985681, 0.81206717, 1.30897476, 0.69429295, 1.13429026] }, { "type": "double", "attributes": {}, - "value": [0.90955296, 0.91327642, 0.94144586, 0.88740075, 1.13423741] + "value": [1.08282103, 1.04907781, 1.22490253, 0.96461857, 0.69119988] }, { "type": "double", "attributes": {}, - "value": [0.86842807, 1.49968682, 0.86587879, 0.83801835, 0.73097926] + "value": [0.86576206, 0.83780528, 0.64335045, 1.38616205, 0.97889898] }, { "type": "double", "attributes": {}, - "value": [0.95767224, 0.99152793, 0.91371265, 1.01865531, 0.81805866] + "value": [1.13819508, 1.20735043, 0.99659683, 0.87374124, 1.18867275] }, { "type": "double", "attributes": {}, - "value": [0.82948728, 0.90313257, 0.87635691, 1.15363459, 0.90893291] + "value": [0.83323897, 1.42889819, 0.85823747, 1.07235653, 1.04590616] }, { "type": "double", "attributes": {}, - "value": [0.98826892, 1.36677832, 0.73226091, 0.83367109, 1.32521422] + "value": [1.29635374, 1.12101089, 1.18691419, 1.0382293, 1.27289693] }, { "type": "double", "attributes": {}, - "value": [1.05873612, 1.13071276, 1.14216503, 0.64273068, 0.70458799] + "value": [0.98076798, 0.77386533, 1.22584739, 0.76950829, 0.95339691] }, { "type": "double", "attributes": {}, - "value": [1.04153588, 0.71443665, 1.1252644, 0.94748645, 1.1025705] + "value": [0.87212613, 1.19741481, 0.92025217, 0.97237525, 0.77017054] }, { "type": "double", "attributes": {}, - 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0.73292771, 1.17285559] }, { "type": "double", "attributes": {}, - "value": [1.1344501, 0.98443236, 1.13375315, 1.20999502, 1.27016243] + "value": [0.6541665, 0.87389629, "NA", 0.98704296, 0.89822526] }, { "type": "double", "attributes": {}, - "value": [0.78204991, 1.07015584, 0.71224649, 0.94610723, 1.85328681] + "value": [0.68478309, 1.19514308, "NA", 1.19565123, 1.12823353] } ] } @@ -20102,7 +20102,7 @@ { "type": "double", "attributes": {}, - "value": [0.83953868, 0.91588217, 1.06571987, 0.90559891, 0.84006488] + "value": [1.29132805, 0.99766874, 0.92202266, 0.97147169, 1.24692569] }, { "type": "logical", @@ -20120,11 +20120,6 @@ "type": "character", "attributes": {}, "value": ["psrf", "mpsrf"] - }, - "class": { - "type": "character", - "attributes": {}, - "value": ["gelman.diag"] } }, "value": [ @@ -20183,7 +20178,7 @@ { "type": "double", "attributes": {}, - "value": [1.11224369, 1.0134272, 0.94738761, 0.8710692, "NA"] + "value": [0.85713661, 0.99504745, 1.59755113, 0.94067038, 1.5918051] }, { "type": "logical", @@ -20201,11 +20196,6 @@ "type": "character", "attributes": {}, "value": ["psrf", "mpsrf"] - }, - "class": { - "type": "character", - "attributes": {}, - "value": ["gelman.diag"] } }, "value": [ @@ -20264,7 +20254,7 @@ { "type": "double", "attributes": {}, - "value": [1.34708698, 1.17052848, 0.92082335, 0.77482061, 1.10078572] + "value": [1.26994619, 1.11496927, 1.22484032, 0.78260032, 1.02935147] }, { "type": "logical", @@ -20282,11 +20272,6 @@ "type": "character", "attributes": {}, "value": ["psrf", "mpsrf"] - }, - "class": { - "type": "character", - "attributes": {}, - "value": ["gelman.diag"] } }, "value": [ @@ -20327,11 +20312,6 @@ "type": "character", "attributes": {}, "value": ["psrf", "mpsrf"] - }, - "class": { - "type": "character", - "attributes": {}, - "value": ["gelman.diag"] } }, "value": [ @@ -20390,12 +20370,12 @@ { "type": "double", "attributes": {}, - "value": [1.0033329, 0.9216911, 1.04504697, 0.88010213, "NA"] + "value": [0.88871014, 0.88146824, 1.1264103, 0.96660343, 0.77642176] }, { "type": "logical", "attributes": {}, - "value": [false, true] + "value": [true, false] }, { "type": "list", @@ -20408,11 +20388,6 @@ "type": "character", "attributes": {}, "value": ["psrf", "mpsrf"] - }, - "class": { - "type": "character", - "attributes": {}, - "value": ["gelman.diag"] } }, "value": [ @@ -20439,7 +20414,7 @@ ] } }, - "value": [1.08588305, 1.2273234] + "value": [1.06626227, 1.09796743] }, { "type": "NULL" @@ -20453,11 +20428,6 @@ "type": "character", "attributes": {}, "value": ["psrf", "mpsrf"] - }, - "class": { - "type": "character", - "attributes": {}, - "value": ["gelman.diag"] } }, "value": [ @@ -20484,7 +20454,7 @@ ] } }, - "value": [1.06626227, 1.09796743] + "value": [1.08588305, 1.2273234] }, { "type": "NULL" @@ -20541,12 +20511,12 @@ { "type": "double", "attributes": {}, - "value": [1.12670783, 0.92096564, 0.93871778, 0.92311394, "NA"] + "value": [0.48364105, 0.93722817, 0.70361866, 0.84555007, 0.38715794] }, { "type": "logical", "attributes": {}, - "value": [false, true] + "value": [true, false] }, { "type": "list", @@ -20559,11 +20529,6 @@ "type": "character", "attributes": {}, "value": ["psrf", "mpsrf"] - }, - "class": { - "type": "character", - "attributes": {}, - "value": ["gelman.diag"] } }, "value": [ @@ -20590,7 +20555,7 @@ ] } }, - "value": [1.30138256, 2.76090238] + "value": [1.02180146, 1.04177601] }, { "type": "NULL" @@ -20604,11 +20569,6 @@ "type": "character", "attributes": {}, "value": ["psrf", "mpsrf"] - }, - "class": { - "type": "character", - "attributes": {}, - "value": ["gelman.diag"] } }, "value": [ @@ -20635,7 +20595,7 @@ ] } }, - "value": [1.02180146, 1.04177601] + "value": [1.30138256, 2.76090238] }, { "type": "NULL" @@ -20667,7 +20627,7 @@ { "type": "double", "attributes": {}, - "value": [0.74513841, 0.93045526, 1.07865269, 0.79847393, 1.99595141] + "value": [1.11202284, 1.62377233, 0.97466746, 1.4155541, 0.97185051] }, { "type": "logical", @@ -20685,11 +20645,6 @@ "type": "character", "attributes": {}, "value": ["psrf", "mpsrf"] - }, - "class": { - "type": "character", - "attributes": {}, - "value": ["gelman.diag"] } }, "value": [ @@ -20730,11 +20685,6 @@ "type": "character", "attributes": {}, "value": ["psrf", "mpsrf"] - }, - "class": { - "type": "character", - "attributes": {}, - "value": ["gelman.diag"] } }, "value": [ @@ -20793,7 +20743,7 @@ { "type": "double", "attributes": {}, - "value": [0.91545587, 1.26842295, 0.65674867, 1.41384318, "NA"] + "value": [1.31434356, 0.6535476, 0.57880618, 1.11860124, 1.20305732] }, { "type": "logical", @@ -20811,11 +20761,6 @@ "type": "character", "attributes": {}, "value": ["psrf", "mpsrf"] - }, - "class": { - "type": "character", - "attributes": {}, - "value": ["gelman.diag"] } }, "value": [ @@ -20874,7 +20819,7 @@ { "type": "double", "attributes": {}, - "value": [1.46667941, 0.92564963, 0.72628082, 0.8210093, 1.39432385] + "value": [1.02125246, 1.53469409, 1.14810298, 1.46667941, 0.92564963] }, { "type": "logical", @@ -20892,11 +20837,6 @@ "type": "character", "attributes": {}, "value": ["psrf", "mpsrf"] - }, - "class": { - "type": "character", - "attributes": {}, - "value": ["gelman.diag"] } }, "value": [ @@ -20950,12 +20890,12 @@ { "type": "double", "attributes": {}, - "value": [1.50002569, 1.49999116, 1.50000881, 1.5000003, 1.50000572] + "value": [1.50000311, 1.49999275, 1.49999859, 1.50003524, 1.50000421] }, { "type": "double", "attributes": {}, - "value": [1.49998719, 1.09996732, 1.09960771, 1.09999027, 1.49998392] + "value": ["NA", 1.49999502, 1.00473161, 1.49971186, 1.1000197] }, { "type": "logical", @@ -20973,11 +20913,6 @@ "type": "character", "attributes": {}, "value": ["psrf", "mpsrf"] - }, - "class": { - "type": "character", - "attributes": {}, - "value": ["gelman.diag"] } }, "value": [ @@ -21004,7 +20939,7 @@ ] } }, - "value": [1.21752296, 1.7587419] + "value": [1.01872211, 1.13081299] }, { "type": "NULL" @@ -21036,7 +20971,7 @@ { "type": "double", "attributes": {}, - "value": [0.95435841, 1.31424638, 0.64631375, 1.02244546, 1.14068966] + "value": [0.91800364, 0.85016542, 0.71860597, 1.07449868, 1.30864555] }, { "type": "logical", @@ -21054,11 +20989,6 @@ "type": "character", "attributes": {}, "value": ["psrf", "mpsrf"] - }, - "class": { - "type": "character", - "attributes": {}, - "value": ["gelman.diag"] } }, "value": [ @@ -21117,7 +21047,7 @@ { "type": "double", "attributes": {}, - "value": [1.02441156, 1.44535094, 1.0483692, 0.86256664, 0.77284348] + "value": ["NA", 0.95176321, 1.13296226, 1.02441156, 1.44535094] }, { "type": "logical", @@ -21135,11 +21065,6 @@ "type": "character", "attributes": {}, "value": ["psrf", "mpsrf"] - }, - "class": { - "type": "character", - "attributes": {}, - "value": ["gelman.diag"] } }, "value": [ @@ -21180,11 +21105,6 @@ "type": "character", "attributes": {}, "value": ["psrf", "mpsrf"] - }, - "class": { - "type": "character", - "attributes": {}, - "value": ["gelman.diag"] } }, "value": [ From 57786c22b41ff57c38a9ce2442a22ac5d953e226 Mon Sep 17 00:00:00 2001 From: Steffi LaZerte Date: Tue, 2 Jun 2026 14:56:38 -0500 Subject: [PATCH 13/31] Fix lintr problems in test files --- tests/testthat/test-wallinga_teunis.R | 60 ++++++++------------------- 1 file changed, 17 insertions(+), 43 deletions(-) diff --git a/tests/testthat/test-wallinga_teunis.R b/tests/testthat/test-wallinga_teunis.R index 6014e887..b5421d07 100644 --- a/tests/testthat/test-wallinga_teunis.R +++ b/tests/testthat/test-wallinga_teunis.R @@ -43,11 +43,7 @@ test_that( msg ) - } -) - - - +}) test_that("wallinga_teunis(): results have the right shape/format", { @@ -61,18 +57,13 @@ test_that("wallinga_teunis(): results have the right shape/format", { n_sim = 50) ) - expect_equal(class(res), "wallinga_teunis") - expect_true(is.list(res)) + expect_s3_class(res, "wallinga_teunis") + expect_type(res, "list") exp_names <- c("R", "method", "si_distr", "SI.Moments", "dates", "I", "I_local", "I_imported") - expect_identical(names(res), exp_names) + expect_named(res, exp_names) expect_identical(dim(res$R), c(99L, 6L)) - -} -) - - - +}) test_that("wallinga_teunis() gives expected results with flat incidence", { @@ -91,13 +82,9 @@ test_that("wallinga_teunis() gives expected results with flat incidence", { ### expect R estimates to be ~1 except very early and very late ones expect_equal(mean(res$R$`Mean(R)`[10:80]), 1) - }) - - - test_that("wallinga_teunis() gives expected results with increasing incidence", { ## Expected results @@ -124,15 +111,11 @@ test_that("wallinga_teunis() gives expected results with increasing incidence", n_sim = 50) ) - expect_true(res_1$R$`Quantile.0.025(R)` > 1) - expect_true(res_2$R$`Quantile.0.025(R)` > res_1$R$`Quantile.0.975(R)`) - + expect_gt(res_1$R$`Quantile.0.025(R)`, 1) + expect_gt(res_2$R$`Quantile.0.025(R)`, res_1$R$`Quantile.0.975(R)`) }) - - - test_that("wallinga_teunis() works with data.frame inputs", { ## We test that results are the same as with a numeric vector with identical @@ -143,7 +126,8 @@ test_that("wallinga_teunis() works with data.frame inputs", { res_i <- wallinga_teunis( i, method = "non_parametric_si", - config = list(t_start = 10, t_end = 90, + config = list(t_start = 10, + t_end = 90, si_distr = Flu2009$si_distr, seed = 1, n_sim = 50) @@ -151,7 +135,8 @@ test_that("wallinga_teunis() works with data.frame inputs", { res_df <- wallinga_teunis( df, method = "non_parametric_si", - config = list(t_start = 10, t_end = 90, + config = list(t_start = 10, + t_end = 90, si_distr = Flu2009$si_distr, seed = 1, n_sim = 50), @@ -165,9 +150,6 @@ test_that("wallinga_teunis() works with data.frame inputs", { }) - - - test_that("wallinga_teunis() works with incidence inputs", { ## We test that results are the same for an incidence object as for the @@ -198,11 +180,7 @@ test_that("wallinga_teunis() works with incidence inputs", { expect_equal(res_df$dates, df$dates) expect_equal(res_incid$I, res_df$I) -} -) - - - +}) test_that("wallinga_teunis() works with incidence2 inputs", { @@ -226,9 +204,9 @@ test_that("wallinga_teunis() works with incidence2 inputs", { res_df <- wallinga_teunis( df, "count", method = "non_parametric_si", - config = list(t_start = 10, t_end = 90, + config = list(t_start = 10, + t_end = 90, si_distr = Flu2009$si_distr, - seed = 1, n_sim = 50, seed = 1) ) @@ -237,11 +215,7 @@ test_that("wallinga_teunis() works with incidence2 inputs", { expect_equal(res_incid$dates, res_df$dates) expect_equal(res_incid$I, res_df$I) -} -) - - - +}) test_that( @@ -292,8 +266,8 @@ test_that( expect_equal(out1$R$`Mean(R)`, out4$R$`Mean(R)`) expect_true(any(out1$R$`Quantile.0.025(R)` != out4$R$`Quantile.0.025(R)`)) expect_true(any(out1$R$`Quantile.0.975(R)` != out4$R$`Quantile.0.975(R)`)) - } -) +}) + test_that("Example 1 matches saved output", { data("Flu2009") From c3dc2d98a041fab604a170c6d05cad10e19cc7b8 Mon Sep 17 00:00:00 2001 From: Steffi LaZerte Date: Tue, 2 Jun 2026 16:01:53 -0500 Subject: [PATCH 14/31] Update gibs_draws snaps --- tests/testthat/_snaps/gibs_draws.md | 41 +++++++++++++++++++++++++++++ 1 file changed, 41 insertions(+) diff --git a/tests/testthat/_snaps/gibs_draws.md b/tests/testthat/_snaps/gibs_draws.md index 31dd4da3..2aebb23e 100644 --- a/tests/testthat/_snaps/gibs_draws.md +++ b/tests/testthat/_snaps/gibs_draws.md @@ -1,3 +1,44 @@ +# process_I_multivariant() + + { + "type": "list", + "attributes": { + "names": { + "type": "character", + "attributes": {}, + "value": ["local", "imported"] + } + }, + "value": [ + { + "type": "double", + "attributes": {}, + "value": [10, 10, 10, 10, 10] + }, + { + "type": "double", + "attributes": {}, + "value": [0, 0, 0, 0, 0] + } + ] + } + +# compute_lambda() + + { + "type": "double", + "attributes": {}, + "value": [10, 10, 10, 10, 10] + } + +# draw_R() + + { + "type": "double", + "attributes": {}, + "value": [1.34883043, 0.84282641, 0.79784422, 1.38220267, 0.78447105] + } + # estimate_advantage() { From 36fbdf91aa11e88d3ea5fa3bc004dad5d7fd769f Mon Sep 17 00:00:00 2001 From: Steffi LaZerte Date: Tue, 2 Jun 2026 16:02:33 -0500 Subject: [PATCH 15/31] Fix warnings in plot and vignette from size -> linewidth --- R/plot.R | 2 +- vignettes/EpiEstim_backimputation.Rmd | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/R/plot.R b/R/plot.R index f382a3d8..3658686b 100644 --- a/R/plot.R +++ b/R/plot.R @@ -177,7 +177,7 @@ plot.estimate_R <- function(x, what = c("all", "incid", "R", "SI"), plot_theme = panel.border = element_blank(), axis.line = element_line( colour = "black", - size = 0.2 + linewidth = 0.2 ), plot.title = element_text( size = 12, diff --git a/vignettes/EpiEstim_backimputation.Rmd b/vignettes/EpiEstim_backimputation.Rmd index 86b0971d..5f267234 100644 --- a/vignettes/EpiEstim_backimputation.Rmd +++ b/vignettes/EpiEstim_backimputation.Rmd @@ -24,7 +24,7 @@ theme_epiestim <- function() { panel.border = element_blank(), axis.line = element_line( colour = "black", - size = 0.2 + linewidth = 0.2 ), plot.title = element_text( size = 12, From b73dcfa802a8cabea0062cee0f578c849edac324 Mon Sep 17 00:00:00 2001 From: Steffi LaZerte Date: Tue, 2 Jun 2026 16:08:17 -0500 Subject: [PATCH 16/31] Quieten noisy tests after update to testthat edition 3 --- tests/testthat/test-back-compatibility.R | 13 +- tests/testthat/test-backimpute.R | 19 +- tests/testthat/test-multivariant-input.R | 9 +- tests/testthat/test-plot.R | 5 +- tests/testthat/test-sample_posterior_R.R | 3 +- tests/testthat/test-samplers.R | 14 +- tests/testthat/test-weekly.R | 360 ++++++++++++++++------- 7 files changed, 283 insertions(+), 140 deletions(-) diff --git a/tests/testthat/test-back-compatibility.R b/tests/testthat/test-back-compatibility.R index 9e935541..57df9f9e 100644 --- a/tests/testthat/test-back-compatibility.R +++ b/tests/testthat/test-back-compatibility.R @@ -5,8 +5,7 @@ test_that("These things still calculate the same after refactoring", { Flu2009$incidence, method = "non_parametric_si", config = make_config(list(si_distr = Flu2009$si_distr)) - ) - + ) |> suppressMessages() expect_snapshot_value(x1, style = "json2") @@ -14,15 +13,13 @@ test_that("These things still calculate the same after refactoring", { Flu2009$incidence$I, method = "non_parametric_si", config = make_config(list(si_distr = Flu2009$si_distr)) - ) + ) |> suppressMessages() expect_snapshot_value(x2, style = "json2") x3 <- estimate_R( data.frame(Flu2009$incidence$I), method = "non_parametric_si", config = make_config(list(si_distr = Flu2009$si_distr)) - ) - expect_snapshot_value(x3, style = "json2") - - -}) \ No newline at end of file + ) |> suppressMessages() + expect_snapshot_value(x3, style = "json2") +}) diff --git a/tests/testthat/test-backimpute.R b/tests/testthat/test-backimpute.R index 113ffe43..b282f205 100644 --- a/tests/testthat/test-backimpute.R +++ b/tests/testthat/test-backimpute.R @@ -59,12 +59,13 @@ test_that("warnings and errors are working as expected", { expect_warning( estimate_R(incid = incid_covid, backimputation_window = 5, config = config_covid2), "Estimate of the growth rate is negative, consider removing backimputation, or extending the backimputation window" - ) + ) |> expect_warning("The backimputation window is short") expect_warning( estimate_R(incid = incid_covid, backimputation_window = 3, config = config_covid), "The backimputation window is short and may lead to an inaccurate estimate of the growth rate." - ) + ) |> expect_warning("Estimate of the growth rate") |> + suppressMessages() }) @@ -78,12 +79,14 @@ test_that("estimate_R outputs shouldn't depend on the format of the incidence da res_1 <- estimate_R(Flu2009$incidence, method = "parametric_si", backimputation_window = 6, - config = config_flu)$R + config = config_flu)$R |> + suppressMessages() res_2 <- estimate_R(Flu2009$incidence$I, method = "parametric_si", backimputation_window = 6, - config = config_flu)$R + config = config_flu)$R |> + suppressMessages() # all formats should produce identical R estimates expect_equal(drop_date_cols(res_1), drop_date_cols(res_2)) @@ -125,7 +128,7 @@ test_that("outputs are working as expected", { expect_equal( estimate_R(incid = incid_covid, backimputation_window = 0, config = config_covid), estimate_R(incid = incid_covid, config = config_covid) - ) + ) |> suppressMessages() # check adjusted estimates are lower than original... expect_lt({ @@ -139,7 +142,7 @@ test_that("outputs are working as expected", { adjusted-original }, 0 - ) + ) |> suppressMessages() # ... even if growth rate is negative! suppressWarnings( @@ -153,7 +156,7 @@ test_that("outputs are working as expected", { config = config_covid)$R[1, "Mean(R)"] adjusted-original }, 0) - ) + ) |> suppressMessages() expect_lt({ R_constant <- estimate_R( @@ -162,7 +165,7 @@ test_that("outputs are working as expected", { config = config_constant, method = "parametric_si") diff(range(R_constant$R$`Mean(R)`)) - }, 1e-6) + }, 1e-6) |> suppressMessages() # Check class(incid) == "data.frame" doesn't result in error expect_silent( diff --git a/tests/testthat/test-multivariant-input.R b/tests/testthat/test-multivariant-input.R index ef1d5823..53156a89 100644 --- a/tests/testthat/test-multivariant-input.R +++ b/tests/testthat/test-multivariant-input.R @@ -272,7 +272,8 @@ test_that("convergence check returns FALSE for non-converging MCMC", { out <- estimate_advantage(incid=incid, si_distr=si_distr, priors=priors, mcmc_control = low_iter(), t_min = 2L, t_max = nrow(incid), - seed = NULL) + seed = NULL) |> + suppressMessages() expect_false(out$convergence[1]) }) @@ -308,7 +309,8 @@ test_that("estimate of epsilon is the same with or without incidence reordering" out2 <- estimate_advantage(incid=incid, si_distr=si_for_est, priors=priors, mcmc_control = default_mcmc_controls(), t_min = 2L, t_max = nrow(incid), - seed = NULL, reorder_incid = FALSE) + seed = NULL, reorder_incid = FALSE)|> + suppressMessages() expect_equal(mean(out$epsilon), mean(out2$epsilon), tolerance = 0.001) }) @@ -324,7 +326,8 @@ test_that("the Rt of the reference variant is returned both with and without inc out2 <- estimate_advantage(incid=incid, si_distr=si_for_est, priors=priors, mcmc_control = default_mcmc_controls(), t_min = 2L, t_max = nrow(incid), - seed = NULL, reorder_incid = FALSE) + seed = NULL, reorder_incid = FALSE) |> + suppressMessages() expect_equal(stats::median(out$R, na.rm = T), stats::median(out2$R, na.rm = T), tolerance = 0.001) }) \ No newline at end of file diff --git a/tests/testthat/test-plot.R b/tests/testthat/test-plot.R index 2eee7207..8318b217 100644 --- a/tests/testthat/test-plot.R +++ b/tests/testthat/test-plot.R @@ -28,7 +28,7 @@ test_that("plot.estimate_R doesn't have to include the legend", { "Flu2009-instantaneous-no-legend", plot(R_i, legend = FALSE), variant = system - ) + ) |> suppressWarnings() # TODO: Fix use of aes_string in incidence to avoid this warning }) test_that("incidence can be plotted separately with imported cases", { @@ -36,7 +36,7 @@ test_that("incidence can be plotted separately with imported cases", { "Flu2009-incidence-import", plot(R_i, "incid", add_imported_cases=TRUE), variant = system - ) + ) |> suppressMessages() }) test_that("serial interval distribution can be plotted separately", { @@ -59,3 +59,4 @@ test_that("Reproduction numbers can be plotted separately", { variant = system ) }) + diff --git a/tests/testthat/test-sample_posterior_R.R b/tests/testthat/test-sample_posterior_R.R index 6ae52e7b..2a689932 100644 --- a/tests/testthat/test-sample_posterior_R.R +++ b/tests/testthat/test-sample_posterior_R.R @@ -6,7 +6,8 @@ data("Flu2009") ## the reproduction number on sliding weekly windows res <- estimate_R(incid = Flu2009$incidence, method = "non_parametric_si", - config = make_config(list(si_distr = Flu2009$si_distr))) + config = make_config(list(si_distr = Flu2009$si_distr))) |> + suppressMessages() test_that("only estimate_R or wallinga_teunis objects are accepted", { diff --git a/tests/testthat/test-samplers.R b/tests/testthat/test-samplers.R index e6463055..60d4c3fe 100644 --- a/tests/testthat/test-samplers.R +++ b/tests/testthat/test-samplers.R @@ -420,7 +420,8 @@ test_that("estimate_advantage produces expected results (>2 variants 1 loc)", { w_v <- c(0, 0.2, 0.5, 0.3) si_distr <- cbind(w_v, w_v, w_v) - x <- estimate_advantage(incid, si_distr, priors, seed = 1, t_min = 2L) + x <- estimate_advantage(incid, si_distr, priors, seed = 1, t_min = 2L) |> + suppressMessages() ## epsilon should be approximately 1 expect_equal( @@ -516,7 +517,8 @@ test_that("estimate_advantage produces expected results (>2var, 1loc, imports)", si_distr <- cbind(w_v, w_v, w_v) x <- estimate_advantage(incid, si_distr, priors, seed = 1, - incid_imported = incid_imported, t_min = 2L) + incid_imported = incid_imported, t_min = 2L) |> + suppressMessages() ## epsilon should be approximately 0 expect_equal( @@ -557,7 +559,7 @@ test_that("estimate_advantage produces expected results (>2var, 4loc, imports)", incid, si_distr, priors, seed = 1, incid_imported = incid_imported, mcmc_control = list(n_iter = 2000L, burnin = 100L, thin = 10L), t_min = 2L - ) + ) |> suppressMessages() ## epsilon should be approximately 0 expect_equal( @@ -704,7 +706,7 @@ test_that("estimate_advantage produces expected results (2 var, 2 loc, R_loc1 = priors <- default_priors() x <- estimate_advantage( incid, si_distr, priors, seed = 1, t_min = 2L - ) + ) |> suppressMessages() ## R should be approx 1.1 for loc1 and 1.5 for loc2 expect_equal(mean(x$R[,1,], na.rm = TRUE), 1.1, tolerance = 0.5) @@ -992,7 +994,7 @@ test_that("estimate_advantage uses the correct t_min", { ## if t_min is NULL, t_min would be set to ## compute_t_min. t_min <- compute_t_min(incid, si_distr) - x <- estimate_advantage(incid, si_distr, priors, seed = 1) + x <- estimate_advantage(incid, si_distr, priors, seed = 1) |> suppressMessages() expect_true(all(is.na(x$R[seq(1, t_min - 1, 1), , ]))) expect_false(anyNA(x$R[seq(t_min, dim(x$R)[1]), , ])) @@ -1018,7 +1020,7 @@ test_that("estimate_advantage convergence checks work with >2 variants", { low_iter <- list(n_iter = 60L, burnin = 10L, thin = 1L) x <- estimate_advantage( incid, si_distr, priors, seed = 1, t_min = 2L, mcmc_control = low_iter - ) + ) |> suppressMessages() ## convergence should be a list of length 2. ## not checking whether chains have converged or not. ## that is tested in a different set of tests. diff --git a/tests/testthat/test-weekly.R b/tests/testthat/test-weekly.R index b26004cc..64bc9f2d 100644 --- a/tests/testthat/test-weekly.R +++ b/tests/testthat/test-weekly.R @@ -9,9 +9,9 @@ dt <- 7L dt_vec <- c(2L,2L,3L) dt_vec_1 <- c(2L,1L,3L) -weekly_inc <- aggregate_inc(incid, dt) -agg_inc <- aggregate_inc(incid, dt_vec) -agg_inc_1 <- aggregate_inc(incid, dt_vec_1) +weekly_inc <- aggregate_inc(incid, dt) |> suppressMessages() +agg_inc <- aggregate_inc(incid, dt_vec) |> suppressMessages() +agg_inc_1 <- aggregate_inc(incid, dt_vec_1) |> suppressMessages() mean_si <- sum(SARS2003$si_distr * seq_along(SARS2003$si_distr)) std_si <- @@ -34,11 +34,11 @@ test_that("function to aggregate incidence works", { expect_equal( aggregate_inc(SARS2003$incidence, 7L)[1], sum(SARS2003$incidence[1:7]) - ) + ) |> suppressMessages() expect_equal( sum(aggregate_inc(SARS2003$incidence, dt_vec)[1:3]), sum(SARS2003$incidence[1:7]) - ) + ) |> suppressMessages() }) @@ -55,36 +55,46 @@ test_that("estimate_R_agg works in parametric mode", { iter = 10L, config = config, method = method, - grid = list(precision = 0.001, min = -1, max = 1))) + grid = list(precision = 0.001, min = -1, max = 1))) |> + suppressMessages() - res_agg1 <- suppressWarnings(estimate_R_agg(incid = agg_inc, + res_agg1 <- estimate_R_agg(incid = agg_inc, dt = dt_vec, dt_out = 7L, iter = 10L, config = config, method = method, - grid = list(precision = 0.001, min = -1, max = 1))) + grid = list(precision = 0.001, min = -1, max = 1)) |> + suppressWarnings() |> + suppressMessages() - res_agg2 <- suppressWarnings(estimate_R_agg(incid = agg_inc, + res_agg2 <- estimate_R_agg( +incid = agg_inc, dt = rep_len(dt_vec, length(agg_inc)), dt_out = 7L, iter = 10L, config = config, method = method, - grid = list(precision = 0.001, min = -1, max = 1))) + grid = list(precision = 0.001, min = -1, max = 1)) |> + suppressWarnings() |> + suppressMessages() - res_agg3 <- suppressWarnings(estimate_R_agg(incid = agg_inc_1, + res_agg3 <- estimate_R_agg( + incid = agg_inc_1, dt = dt_vec_1, dt_out = 7L, iter = 10L, config = config, method = method, - grid = list(precision = 0.001, min = -1, max = 1))) + grid = list(precision = 0.001, min = -1, max = 1)) |> + suppressWarnings() |> + suppressMessages() res_daily <- suppressWarnings(estimate_R(incid = incid, config = config, - method = method)) + method = method)) |> + suppressMessages() ###################################################################### ## test that the weekly incidence matches the aggregated daily one @@ -148,7 +158,8 @@ test_that("estimate_R_agg works in parametric mode", { res_daily_reconstructed <- suppressWarnings(estimate_R(incid = res_weekly$I, config = config, - method = method)) + method = method)) |> + suppressMessages() common_t_start <- intersect(res_daily_reconstructed$R$t_start, res_weekly$R$t_start) @@ -172,41 +183,57 @@ test_that("estimate_R_agg works in non-parametric mode", { ## the reproduction number on sliding weekly windows method <- "non_parametric_si" config <- make_config(list(si_distr = si_distr)) - res_weekly <- suppressWarnings(estimate_R_agg(incid = weekly_inc, + res_weekly <- estimate_R_agg( + incid = weekly_inc, dt = 7L, dt_out = 7L, iter = 10L, config = config, method = method, - grid = list(precision = 0.001, min = -1, max = 1))) + grid = list(precision = 0.001, min = -1, max = 1) + ) |> + suppressWarnings() |> + suppressMessages() - res_agg1 <- suppressWarnings(estimate_R_agg(incid = agg_inc, + res_agg1 <- estimate_R_agg( + incid = agg_inc, dt = dt_vec, dt_out = 7L, iter = 10L, config = config, method = method, - grid = list(precision = 0.001, min = -1, max = 1))) + grid = list(precision = 0.001, min = -1, max = 1) + ) |> + suppressWarnings() |> + suppressMessages() - res_agg2 <- suppressWarnings(estimate_R_agg(incid = agg_inc, + res_agg2 <- estimate_R_agg( + incid = agg_inc, dt = rep_len(dt_vec, length(agg_inc)), dt_out = 7L, iter = 10L, config = config, method = method, - grid = list(precision = 0.001, min = -1, max = 1))) + grid = list(precision = 0.001, min = -1, max = 1) + ) |> + suppressWarnings() |> + suppressMessages() - res_agg3 <- suppressWarnings(estimate_R_agg(incid = agg_inc_1, + res_agg3 <- estimate_R_agg( + incid = agg_inc_1, dt = dt_vec_1, dt_out = 7L, iter = 10L, config = config, method = method, - grid = list(precision = 0.001, min = -1, max = 1))) + grid = list(precision = 0.001, min = -1, max = 1) + ) |> + suppressWarnings() |> + suppressMessages() - res_daily <- suppressWarnings(estimate_R(incid = incid, - config = config, - method = method)) + res_daily <- estimate_R(incid = incid, config = config, method = method) |> + suppressWarnings() |> + suppressMessages() ###################################################################### ## test that aggregated incidence matches the aggregated daily one @@ -268,9 +295,13 @@ test_that("estimate_R_agg works in non-parametric mode", { ## test that the weekly R estimates from weekly data match exactly the weekly ## R estimates from the daily reconstructed incidence - res_daily_reconstructed <- suppressWarnings(estimate_R(incid = res_weekly$I, + res_daily_reconstructed <- estimate_R( + incid = res_weekly$I, config = config, - method = method)) + method = method + ) |> + suppressWarnings() |> + suppressMessages() common_t_start <- intersect(res_daily_reconstructed$R$t_start, res_weekly$R$t_start) @@ -292,27 +323,35 @@ test_that("estimate_R works with aggregated data in parametric mode", { ## when not specifying t_start and t_end in config, they are set to estimate ## the reproduction number on sliding weekly windows method <- "parametric_si" - config <- make_config(list(mean_si = mean_si, - std_si = std_si)) - res_weekly <- suppressWarnings(estimate_R(incid = weekly_inc, + config <- make_config(list(mean_si = mean_si, std_si = std_si)) + + res_weekly <- estimate_R( + incid = weekly_inc, dt = 7L, dt_out = 7L, iter = 10L, config = config, method = method, - grid = list(precision = 0.001, min = -1, max = 1))) + grid = list(precision = 0.001, min = -1, max = 1) + ) |> + suppressWarnings() |> + suppressMessages() - res_weekly_agg <- suppressWarnings(estimate_R_agg(incid = weekly_inc, + res_weekly_agg <- estimate_R_agg( + incid = weekly_inc, dt = 7L, dt_out = 7L, iter = 10L, config = config, method = method, - grid = list(precision = 0.001, min = -1, max = 1))) - - res_daily <- suppressWarnings(estimate_R(incid = incid, - config = config, - method = method)) + grid = list(precision = 0.001, min = -1, max = 1) + ) |> + suppressWarnings() |> + suppressMessages() + + res_daily <- estimate_R(incid = incid, config = config, method = method) |> + suppressWarnings() |> + suppressMessages() ###################################################################### ## test that the weekly incidence matches the aggregated daily one @@ -342,9 +381,13 @@ test_that("estimate_R works with aggregated data in parametric mode", { ## test that the weekly R estimates from weekly data match exactly the weekly ## R estimates from the daily reconstructed incidence - res_daily_reconstructed <- suppressWarnings(estimate_R(incid = res_weekly$I, + res_daily_reconstructed <- estimate_R( + incid = res_weekly$I, config = config, - method = method)) + method = method + ) |> + suppressWarnings() |> + suppressMessages() common_t_start <- intersect(res_daily_reconstructed$R$t_start, res_weekly$R$t_start) @@ -370,25 +413,34 @@ test_that("estimate_R works with aggregated data in non-parametric mode", { ## the reproduction number on sliding weekly windows method <- "non_parametric_si" config <- make_config(list(si_distr = si_distr)) - res_weekly <- suppressWarnings(estimate_R(incid = weekly_inc, + + res_weekly <- estimate_R( + incid = weekly_inc, dt = 7L, dt_out = 7L, iter = 10L, config = config, method = method, - grid = list(precision = 0.001, min = -1, max = 1))) + grid = list(precision = 0.001, min = -1, max = 1) + ) |> + suppressWarnings() |> + suppressMessages() - res_weekly_agg <- suppressWarnings(estimate_R_agg(incid = weekly_inc, + res_weekly_agg <- estimate_R_agg( + incid = weekly_inc, dt = 7L, dt_out = 7L, iter = 10L, config = config, method = method, - grid = list(precision = 0.001, min = -1, max = 1))) - - res_daily <- suppressWarnings(estimate_R(incid = incid, - config = config, - method = method)) + grid = list(precision = 0.001, min = -1, max = 1) + ) |> + suppressWarnings() |> + suppressMessages() + + res_daily <- estimate_R(incid = incid, config = config, method = method) |> + suppressWarnings() |> + suppressMessages() ###################################################################### ## test that the weekly incidence matches the aggregated daily one @@ -418,9 +470,13 @@ test_that("estimate_R works with aggregated data in non-parametric mode", { ## test that the weekly R estimates from weekly data match exactly the weekly ## R estimates from the daily reconstructed incidence - res_daily_reconstructed <- suppressWarnings(estimate_R(incid = res_weekly$I, + res_daily_reconstructed <- estimate_R( + incid = res_weekly$I, config = config, - method = method)) + method = method + ) |> + suppressWarnings() |> + suppressMessages() common_t_start <- intersect(res_daily_reconstructed$R$t_start, res_weekly$R$t_start) @@ -441,42 +497,51 @@ test_that("estimate_R works with aggregated data in non-parametric mode", { test_that("aggregated reconstructed incidence matches original input", { method <- "parametric_si" - config <- make_config(list(mean_si = mean_si, - std_si = std_si)) + config <- make_config(list(mean_si = mean_si, std_si = std_si)) - res_agg <- suppressWarnings(estimate_R(incid = agg_inc, + res_agg <- estimate_R( + incid = agg_inc, dt = dt_vec, dt_out = 7L, iter = 10L, config = config, method = method, - grid = list(precision = 0.001, min = -1, max = 1))) + grid = list(precision = 0.001, min = -1, max = 1) + ) |> + suppressWarnings() |> + suppressMessages() - res_agg_1 <- suppressWarnings(estimate_R(incid = agg_inc_1, + res_agg_1 <- estimate_R( + incid = agg_inc_1, dt = dt_vec_1, dt_out = 7L, iter = 10L, config = config, method = method, - grid = list(precision = 0.001, min = -1, max = 1))) + grid = list(precision = 0.001, min = -1, max = 1) + ) |> + suppressWarnings() |> + suppressMessages() - res_weekly <- suppressWarnings(estimate_R(incid = weekly_inc, + res_weekly <- estimate_R( + incid = weekly_inc, dt = 7L, config = config, - method = method)) - + method = method + ) |> + suppressWarnings() |> + suppressMessages() ## Aggregating the reconstructed incidence matches original input - expect_equal(agg_inc, - aggregate_inc(res_agg$I, dt_vec)) - - expect_equal(agg_inc_1, - aggregate_inc(res_agg_1$I, dt_vec_1)) + expect_equal(agg_inc, aggregate_inc(res_agg$I, dt_vec)) |> + suppressMessages() - expect_equal(weekly_inc, - aggregate_inc(res_weekly$I, 7L)) + expect_equal(agg_inc_1, aggregate_inc(res_agg_1$I, dt_vec_1)) |> + suppressMessages() + expect_equal(weekly_inc, aggregate_inc(res_weekly$I, 7L)) |> + suppressMessages() }) @@ -489,20 +554,28 @@ test_that("r grid can be automatically updated with similar results", { config <- make_config(list(mean_si = mean_si, std_si = std_si)) - res_weekly_small_grid <- suppressWarnings(estimate_R_agg(incid = weekly_inc, + res_weekly_small_grid <- estimate_R_agg( + incid = weekly_inc, dt = 7L, dt_out = 7L, iter = 10L, config = config, method = method, - grid = list(precision = 0.001, min = 0.01, max = 0.012))) + grid = list(precision = 0.001, min = 0.01, max = 0.012) +) |> + suppressWarnings() |> + suppressMessages() - res_weekly_default_grid <- suppressWarnings(estimate_R_agg(incid = weekly_inc, + res_weekly_default_grid <- estimate_R_agg( + incid = weekly_inc, dt = 7L, dt_out = 7L, iter = 10L, config = config, - method = method)) + method = method +) |> + suppressWarnings() |> + suppressMessages() expect_lt(max(abs(res_weekly_small_grid$R - res_weekly_default_grid$R)), 1e-9) @@ -629,13 +702,17 @@ test_that("result of weekly version of estimate_R can be plotted without error", method <- "parametric_si" config <- make_config(list(mean_si = mean_si, std_si = std_si)) - res_weekly <- suppressWarnings(estimate_R_agg(incid = weekly_inc, + res_weekly <- estimate_R_agg( + incid = weekly_inc, dt = 7L, dt_out = 7L, iter = 10L, config = config, method = method, - grid = list(precision = 0.001, min = -1, max = 1))) + grid = list(precision = 0.001, min = -1, max = 1) + ) |> + suppressWarnings() |> + suppressMessages() expect_error(suppressWarnings(plot(res_weekly)),NA) @@ -643,39 +720,48 @@ test_that("result of weekly version of estimate_R can be plotted without error", test_that("method works with different single integers of dt", { - - inc_three <- aggregate_inc(incid, 3L) - inc_ten <- aggregate_inc(incid, 10L) + inc_three <- aggregate_inc(incid, 3L) |> suppressMessages() + inc_ten <- aggregate_inc(incid, 10L) |> suppressMessages() method <- "parametric_si" - config <- make_config(list(mean_si = mean_si, - std_si = std_si)) + config <- make_config(list(mean_si = mean_si, std_si = std_si)) - res_daily <- suppressWarnings(estimate_R(incid = incid, - config = config, - method = method)) + res_daily <- estimate_R(incid = incid, config = config, method = method) |> + suppressWarnings() |> + suppressMessages() - - res_three <- suppressWarnings(estimate_R(incid = inc_three, + res_three <- estimate_R( + incid = inc_three, dt = 3L, dt_out = 7L, iter = 10L, config = config, - method = method)) + method = method + ) |> + suppressWarnings() |> + suppressMessages() - res_weekly <- suppressWarnings(estimate_R(incid = weekly_inc, + res_weekly <- estimate_R( + incid = weekly_inc, dt = 7L, dt_out = 7L, iter = 10L, config = config, - method = method)) + method = method + ) |> + suppressWarnings() |> + suppressMessages() - res_ten <- suppressWarnings(estimate_R(incid = inc_ten, + res_ten <- estimate_R( + incid = inc_ten, dt = 10L, dt_out = 7L, iter = 10L, config = config, - method = method)) + method = method + ) |> + suppressWarnings() |> + suppressMessages() ## test that the three and ten day incidence matches the aggregated daily one ## except for first time window where we impose I = 1 on first day @@ -716,20 +802,28 @@ test_that("estimate_R_agg handles different incid input formats consistently", { std_si = std_si)) # integer vector - res_int <- suppressWarnings(estimate_R(incid = weekly_inc, + res_int <- estimate_R( + incid = weekly_inc, dt = 7L, dt_out = 7L, iter = 10L, config = config, - method = method)) + method = method + ) |> + suppressWarnings() |> + suppressMessages() # numeric vector - res_numeric <- suppressWarnings(estimate_R(incid = as.numeric(weekly_inc), + res_numeric <- estimate_R( + incid = as.numeric(weekly_inc), dt = 7L, dt_out = 7L, iter = 10L, config = config, - method = method)) + method = method + ) |> + suppressWarnings() |> + suppressMessages() start_date <- as.Date("2020-01-01") @@ -737,26 +831,35 @@ test_that("estimate_R_agg handles different incid input formats consistently", { weekly_inc_df <- data.frame(I = weekly_inc, dates = seq(start_date, by = "week", length.out = length(weekly_inc))) - res_df <- suppressWarnings(estimate_R(incid = weekly_inc_df, + res_df <- estimate_R( + incid = weekly_inc_df, dt = 7L, dt_out = 7L, iter = 10L, config = config, method = method, - date_convention = "start")) + date_convention = "start" + ) |> + suppressWarnings() |> + suppressMessages() # incidence object inc_obj <- incidence::as.incidence(weekly_inc, dates = seq(start_date, by = "week", length.out = length(weekly_inc)), isoweeks = FALSE) - res_inc_obj <- suppressWarnings(estimate_R(incid = inc_obj, + + res_inc_obj <- estimate_R( + incid = inc_obj, dt = 7L, dt_out = 7L, iter = 10L, config = config, method = method, - date_convention = "start")) + date_convention = "start" + ) |> + suppressWarnings() |> + suppressMessages() # incidence2 object data <- data.frame( @@ -766,24 +869,33 @@ test_that("estimate_R_agg handles different incid input formats consistently", { inc2_obj <- incidence2::incidence(data, date_index = "dates", date_names_to = "dates", counts = "I", count_values_to = "I") - res_inc2_obj <- suppressWarnings(estimate_R(incid = inc2_obj, + + res_inc2_obj <- estimate_R( + incid = inc2_obj, dt = 7L, dt_out = 7L, iter = 10L, config = config, method = method, - date_convention = "start")) + date_convention = "start" + ) |> + suppressWarnings() |> + suppressMessages() ## check it works with incidence2 without naming inc2_obj2 <- incidence2::incidence(data, date_index = "dates", counts = "I") - res_inc2_obj2 <- suppressWarnings(estimate_R(incid = inc2_obj2, + res_inc2_obj2 <- estimate_R( + incid = inc2_obj2, dt = 7L, dt_out = 7L, iter = 10L, config = config, method = method, - date_convention = "start")) + date_convention = "start" + ) |> + suppressWarnings() |> + suppressMessages() # ignore date column drop_date_cols <- function(R) { @@ -822,12 +934,17 @@ test_that("t_start and t_end work when dt is used without specifying dt_out", { t_end = c(15, 30))) expect_no_error( - res <- suppressWarnings(estimate_R(incid = weekly_inc, + res <- estimate_R( + incid = weekly_inc, dt = 7L, iter = 10L, config = config, - method = method)) + method = method ) + ) |> + suppressWarnings() |> + suppressMessages() + # check that R is estimated for exactly the two specified windows expect_equal(res$R$t_start, c(8, 16)) @@ -847,13 +964,12 @@ test_that("date_convention can only be 'start' or 'end'", { t_end = c(15, 30))) expect_error( - res <- suppressWarnings(estimate_R(incid = weekly_inc_df, + res <- estimate_R(incid = weekly_inc_df, dt = 7L, iter = 10L, config = config, method = method, date_convention = "xyz")) - ) }) @@ -868,21 +984,29 @@ test_that("date_convention works with 'start' and 'end'", { t_start = c(8, 16), t_end = c(15, 30))) - res_start <- suppressWarnings(estimate_R(incid = weekly_inc_df, + res_start <- estimate_R( + incid = weekly_inc_df, dt = 7L, iter = 10L, config = config, method = method, - date_convention = "start")) + date_convention = "start" + ) |> + suppressWarnings() |> + suppressMessages() expect_equal(res_start$dates, seq(start_date, by = "day", length.out = length(weekly_inc) * 7)) - res_end <- suppressWarnings(estimate_R(incid = weekly_inc_df, + res_end <- estimate_R( + incid = weekly_inc_df, dt = 7L, iter = 10L, config = config, method = method, - date_convention = "end")) + date_convention = "end" + ) |> + suppressWarnings() |> + suppressMessages() expect_equal(res_end$dates, seq(start_date - 6, by = "day", length.out = length(weekly_inc) * 7)) @@ -899,11 +1023,15 @@ test_that("date_convention defaults to 'end'", { t_start = c(8, 16), t_end = c(15, 30))) - res_end <- suppressWarnings(estimate_R(incid = weekly_inc_df, + res_end <- estimate_R( + incid = weekly_inc_df, dt = 7L, iter = 10L, config = config, - method = method)) + method = method + ) |> + suppressWarnings() |> + suppressMessages() expect_equal(res_end$dates, seq(start_date - 6, by = "day", length.out = length(weekly_inc) * 7)) @@ -923,22 +1051,30 @@ test_that("date_convention works with irregular aggregations", { config <- make_config(list(mean_si = mean_si, std_si = std_si)) - res_start_irr <- suppressWarnings(estimate_R(incid = agg_inc_df, + res_start_irr <- estimate_R( + incid = agg_inc_df, dt = c(3L, 3L, 2L), iter = 10L, config = config, method = method, - date_convention = "start")) + date_convention = "start" + ) |> + suppressWarnings() |> + suppressMessages() expect_equal(res_start_irr$dates, seq(start_date, by = "day", length.out = nrow(agg_inc_df)/3 * sum(pattern))) - res_end_irr <- suppressWarnings(estimate_R(incid = agg_inc_df, + res_end_irr <- estimate_R( + incid = agg_inc_df, dt = c(3L, 3L, 2L), iter = 10L, config = config, method = method, - date_convention = "end")) + date_convention = "end" + ) |> + suppressWarnings() |> + suppressMessages() expect_equal(res_end_irr$dates, seq(start_date - (pattern[1] - 1), by = "day", length.out = nrow(agg_inc_df)/3 * sum(pattern))) From 0ef8c9c7c6fef2b095e3a23d2e315e3594d6cb0e Mon Sep 17 00:00:00 2001 From: Steffi LaZerte Date: Tue, 2 Jun 2026 16:18:48 -0500 Subject: [PATCH 17/31] General test cleanup --- tests/testthat/test-backimpute.R | 3 +- tests/testthat/test-multivariant-input.R | 41 +-- tests/testthat/test-samplers.R | 2 - tests/testthat/test-weekly.R | 371 +++++++++++------------ 4 files changed, 196 insertions(+), 221 deletions(-) diff --git a/tests/testthat/test-backimpute.R b/tests/testthat/test-backimpute.R index b282f205..20020af1 100644 --- a/tests/testthat/test-backimpute.R +++ b/tests/testthat/test-backimpute.R @@ -11,7 +11,6 @@ config_covid <- make_config(si_config) config_covid2 <- make_config(c(si_config, t_start=2, t_end=10)) data("Flu2009") -Flu2009$incidence config_flu <- make_config(list( mean_si = 2.6, std_si = 1.5)) incid_constant <- rep(10, 20) @@ -57,7 +56,7 @@ test_that("warnings and errors are working as expected", { ) expect_warning( - estimate_R(incid = incid_covid, backimputation_window = 5, config = config_covid2), + estimate_R(incid = incid_covid, backimputation_window = 5, config = config_covid2), "Estimate of the growth rate is negative, consider removing backimputation, or extending the backimputation window" ) |> expect_warning("The backimputation window is short") diff --git a/tests/testthat/test-multivariant-input.R b/tests/testthat/test-multivariant-input.R index 53156a89..c74470e0 100644 --- a/tests/testthat/test-multivariant-input.R +++ b/tests/testthat/test-multivariant-input.R @@ -1,6 +1,3 @@ -# devtools::load_all() -require(testthat) - ################################ ## Tests for compute_lambda() ## ################################ @@ -68,11 +65,7 @@ test_that("R is specified correctly",{ "R must be >= 0") }) - # seed test - -seed <- "a" - -test_that("seed is specified correctly",{ +test_that("draw_epsilon() seed is specified correctly",{ expect_error(draw_epsilon(R=R, incid=incid, lambda=lambda, priors=priors, t_min = 2L, t_max = nrow(incid), seed = "a"), @@ -112,25 +105,15 @@ test_that("tmin and tmax are specified correctly", { "t_min and t_max must be <= nrow(incid)", fixed=TRUE) }) - - # seed test - -seed <- "a" - -test_that("seed is specified correctly",{ +test_that("draw_R() seed is specified correctly",{ expect_error(draw_R(epsilon=epsilon, incid=incid, lambda=lambda, priors=priors, t_min = 2L, t_max = nrow(incid), - seed = seed), + seed = "a"), "seed must be numeric") }) - - # epsilon test - -epsilon <- -1 - -test_that("epsilon is specified correctly",{ - expect_error(draw_R(epsilon=epsilon, incid=incid, lambda=lambda, priors=priors, +test_that("draw_R() epsilon is specified correctly",{ + expect_error(draw_R(epsilon=-1, incid=incid, lambda=lambda, priors=priors, t_min = 2L, t_max = nrow(incid), seed = NULL), "epsilon must be > 0") @@ -238,13 +221,11 @@ test_that("mcmc_control is specified correctly", { # seed test -seed <- "a" - -test_that("seed is specified correctly",{ +test_that("estimate_advantage() seed is specified correctly",{ expect_error(estimate_advantage(incid=incid, si_distr=si_distr, priors=priors, mcmc_control = default_mcmc_controls(), t_min = 2L, t_max = nrow(incid), - seed = seed), + seed = "a"), "seed must be numeric") }) @@ -292,10 +273,10 @@ test_that("convergence check returns TRUE for converging MCMC", { # use dummy incidence data based on Rt_ref = 1.6, epsilon = 2 -incid <- readRDS("../incidence_files/incid.rds") +incid <- readRDS(testthat::test_path("../incidence_files/incid.rds")) incid <- as.array(incid[[1]]) incid <- incid[[1]] -si_for_est <- readRDS("../incidence_files/si_for_est.rds") +si_for_est <- readRDS(testthat::test_path("../incidence_files/si_for_est.rds")) si_for_est <- si_for_est[[1]] priors <- default_priors() @@ -304,7 +285,7 @@ test_that("estimate of epsilon is the same with or without incidence reordering" out <- estimate_advantage(incid=incid, si_distr=si_for_est, priors=priors, mcmc_control = default_mcmc_controls(), t_min = 2L, t_max = nrow(incid), - seed = NULL, reorder_incid = TRUE) + seed = NULL, reorder_incid = TRUE) out2 <- estimate_advantage(incid=incid, si_distr=si_for_est, priors=priors, mcmc_control = default_mcmc_controls(), @@ -330,4 +311,4 @@ test_that("the Rt of the reference variant is returned both with and without inc suppressMessages() expect_equal(stats::median(out$R, na.rm = T), stats::median(out2$R, na.rm = T), tolerance = 0.001) -}) \ No newline at end of file +}) diff --git a/tests/testthat/test-samplers.R b/tests/testthat/test-samplers.R index 60d4c3fe..5d295867 100644 --- a/tests/testthat/test-samplers.R +++ b/tests/testthat/test-samplers.R @@ -1,5 +1,3 @@ -context("Gibbs samplers") - test_that("draw_epsilon produces expected results (2 variants, 4 locations)", { n_v <- 2 # 2 variants n_loc <- 4 # 4 locations diff --git a/tests/testthat/test-weekly.R b/tests/testthat/test-weekly.R index 64bc9f2d..f080e019 100644 --- a/tests/testthat/test-weekly.R +++ b/tests/testthat/test-weekly.R @@ -1,6 +1,3 @@ -# devtools::load_all() -require(testthat) - data("Flu2009") data("SARS2003") incid <- SARS2003$incidence @@ -59,33 +56,33 @@ test_that("estimate_R_agg works in parametric mode", { suppressMessages() res_agg1 <- estimate_R_agg(incid = agg_inc, - dt = dt_vec, - dt_out = 7L, - iter = 10L, - config = config, - method = method, - grid = list(precision = 0.001, min = -1, max = 1)) |> + dt = dt_vec, + dt_out = 7L, + iter = 10L, + config = config, + method = method, + grid = list(precision = 0.001, min = -1, max = 1)) |> suppressWarnings() |> suppressMessages() res_agg2 <- estimate_R_agg( -incid = agg_inc, - dt = rep_len(dt_vec, length(agg_inc)), - dt_out = 7L, - iter = 10L, - config = config, - method = method, + incid = agg_inc, + dt = rep_len(dt_vec, length(agg_inc)), + dt_out = 7L, + iter = 10L, + config = config, + method = method, grid = list(precision = 0.001, min = -1, max = 1)) |> suppressWarnings() |> suppressMessages() res_agg3 <- estimate_R_agg( incid = agg_inc_1, - dt = dt_vec_1, - dt_out = 7L, - iter = 10L, - config = config, - method = method, + dt = dt_vec_1, + dt_out = 7L, + iter = 10L, + config = config, + method = method, grid = list(precision = 0.001, min = -1, max = 1)) |> suppressWarnings() |> suppressMessages() @@ -185,11 +182,11 @@ test_that("estimate_R_agg works in non-parametric mode", { config <- make_config(list(si_distr = si_distr)) res_weekly <- estimate_R_agg( incid = weekly_inc, - dt = 7L, - dt_out = 7L, - iter = 10L, - config = config, - method = method, + dt = 7L, + dt_out = 7L, + iter = 10L, + config = config, + method = method, grid = list(precision = 0.001, min = -1, max = 1) ) |> suppressWarnings() |> @@ -197,11 +194,11 @@ test_that("estimate_R_agg works in non-parametric mode", { res_agg1 <- estimate_R_agg( incid = agg_inc, - dt = dt_vec, - dt_out = 7L, - iter = 10L, - config = config, - method = method, + dt = dt_vec, + dt_out = 7L, + iter = 10L, + config = config, + method = method, grid = list(precision = 0.001, min = -1, max = 1) ) |> suppressWarnings() |> @@ -209,11 +206,11 @@ test_that("estimate_R_agg works in non-parametric mode", { res_agg2 <- estimate_R_agg( incid = agg_inc, - dt = rep_len(dt_vec, length(agg_inc)), - dt_out = 7L, - iter = 10L, - config = config, - method = method, + dt = rep_len(dt_vec, length(agg_inc)), + dt_out = 7L, + iter = 10L, + config = config, + method = method, grid = list(precision = 0.001, min = -1, max = 1) ) |> suppressWarnings() |> @@ -221,11 +218,11 @@ test_that("estimate_R_agg works in non-parametric mode", { res_agg3 <- estimate_R_agg( incid = agg_inc_1, - dt = dt_vec_1, - dt_out = 7L, - iter = 10L, - config = config, - method = method, + dt = dt_vec_1, + dt_out = 7L, + iter = 10L, + config = config, + method = method, grid = list(precision = 0.001, min = -1, max = 1) ) |> suppressWarnings() |> @@ -294,10 +291,10 @@ test_that("estimate_R_agg works in non-parametric mode", { ###################################################################### ## test that the weekly R estimates from weekly data match exactly the weekly ## R estimates from the daily reconstructed incidence - + res_daily_reconstructed <- estimate_R( incid = res_weekly$I, - config = config, + config = config, method = method ) |> suppressWarnings() |> @@ -327,23 +324,23 @@ test_that("estimate_R works with aggregated data in parametric mode", { res_weekly <- estimate_R( incid = weekly_inc, - dt = 7L, - dt_out = 7L, - iter = 10L, - config = config, - method = method, + dt = 7L, + dt_out = 7L, + iter = 10L, + config = config, + method = method, grid = list(precision = 0.001, min = -1, max = 1) ) |> suppressWarnings() |> suppressMessages() - + res_weekly_agg <- estimate_R_agg( incid = weekly_inc, - dt = 7L, - dt_out = 7L, - iter = 10L, - config = config, - method = method, + dt = 7L, + dt_out = 7L, + iter = 10L, + config = config, + method = method, grid = list(precision = 0.001, min = -1, max = 1) ) |> suppressWarnings() |> @@ -380,10 +377,10 @@ test_that("estimate_R works with aggregated data in parametric mode", { ###################################################################### ## test that the weekly R estimates from weekly data match exactly the weekly ## R estimates from the daily reconstructed incidence - + res_daily_reconstructed <- estimate_R( incid = res_weekly$I, - config = config, + config = config, method = method ) |> suppressWarnings() |> @@ -416,23 +413,23 @@ test_that("estimate_R works with aggregated data in non-parametric mode", { res_weekly <- estimate_R( incid = weekly_inc, - dt = 7L, - dt_out = 7L, - iter = 10L, - config = config, - method = method, + dt = 7L, + dt_out = 7L, + iter = 10L, + config = config, + method = method, grid = list(precision = 0.001, min = -1, max = 1) ) |> suppressWarnings() |> suppressMessages() - + res_weekly_agg <- estimate_R_agg( incid = weekly_inc, - dt = 7L, - dt_out = 7L, - iter = 10L, - config = config, - method = method, + dt = 7L, + dt_out = 7L, + iter = 10L, + config = config, + method = method, grid = list(precision = 0.001, min = -1, max = 1) ) |> suppressWarnings() |> @@ -469,10 +466,10 @@ test_that("estimate_R works with aggregated data in non-parametric mode", { ###################################################################### ## test that the weekly R estimates from weekly data match exactly the weekly ## R estimates from the daily reconstructed incidence - + res_daily_reconstructed <- estimate_R( incid = res_weekly$I, - config = config, + config = config, method = method ) |> suppressWarnings() |> @@ -498,48 +495,48 @@ test_that("estimate_R works with aggregated data in non-parametric mode", { test_that("aggregated reconstructed incidence matches original input", { method <- "parametric_si" config <- make_config(list(mean_si = mean_si, std_si = std_si)) - + res_agg <- estimate_R( incid = agg_inc, - dt = dt_vec, - dt_out = 7L, - iter = 10L, - config = config, - method = method, + dt = dt_vec, + dt_out = 7L, + iter = 10L, + config = config, + method = method, grid = list(precision = 0.001, min = -1, max = 1) ) |> suppressWarnings() |> suppressMessages() - + res_agg_1 <- estimate_R( incid = agg_inc_1, - dt = dt_vec_1, - dt_out = 7L, - iter = 10L, - config = config, - method = method, + dt = dt_vec_1, + dt_out = 7L, + iter = 10L, + config = config, + method = method, grid = list(precision = 0.001, min = -1, max = 1) ) |> suppressWarnings() |> suppressMessages() - + res_weekly <- estimate_R( incid = weekly_inc, - dt = 7L, - config = config, + dt = 7L, + config = config, method = method ) |> suppressWarnings() |> suppressMessages() - + ## Aggregating the reconstructed incidence matches original input - + expect_equal(agg_inc, aggregate_inc(res_agg$I, dt_vec)) |> suppressMessages() - + expect_equal(agg_inc_1, aggregate_inc(res_agg_1$I, dt_vec_1)) |> suppressMessages() - + expect_equal(weekly_inc, aggregate_inc(res_weekly$I, 7L)) |> suppressMessages() }) @@ -556,11 +553,11 @@ test_that("r grid can be automatically updated with similar results", { res_weekly_small_grid <- estimate_R_agg( incid = weekly_inc, - dt = 7L, - dt_out = 7L, - iter = 10L, - config = config, - method = method, + dt = 7L, + dt_out = 7L, + iter = 10L, + config = config, + method = method, grid = list(precision = 0.001, min = 0.01, max = 0.012) ) |> suppressWarnings() |> @@ -568,10 +565,10 @@ test_that("r grid can be automatically updated with similar results", { res_weekly_default_grid <- estimate_R_agg( incid = weekly_inc, - dt = 7L, - dt_out = 7L, - iter = 10L, - config = config, + dt = 7L, + dt_out = 7L, + iter = 10L, + config = config, method = method ) |> suppressWarnings() |> @@ -704,11 +701,11 @@ test_that("result of weekly version of estimate_R can be plotted without error", std_si = std_si)) res_weekly <- estimate_R_agg( incid = weekly_inc, - dt = 7L, - dt_out = 7L, - iter = 10L, - config = config, - method = method, + dt = 7L, + dt_out = 7L, + iter = 10L, + config = config, + method = method, grid = list(precision = 0.001, min = -1, max = 1) ) |> suppressWarnings() |> @@ -725,74 +722,74 @@ test_that("method works with different single integers of dt", { method <- "parametric_si" config <- make_config(list(mean_si = mean_si, std_si = std_si)) - + res_daily <- estimate_R(incid = incid, config = config, method = method) |> suppressWarnings() |> suppressMessages() res_three <- estimate_R( incid = inc_three, - dt = 3L, - dt_out = 7L, - iter = 10L, - config = config, + dt = 3L, + dt_out = 7L, + iter = 10L, + config = config, method = method ) |> suppressWarnings() |> suppressMessages() - + res_weekly <- estimate_R( incid = weekly_inc, - dt = 7L, - dt_out = 7L, - iter = 10L, - config = config, + dt = 7L, + dt_out = 7L, + iter = 10L, + config = config, method = method ) |> suppressWarnings() |> suppressMessages() - + res_ten <- estimate_R( incid = inc_ten, - dt = 10L, - dt_out = 7L, - iter = 10L, - config = config, + dt = 10L, + dt_out = 7L, + iter = 10L, + config = config, method = method ) |> suppressWarnings() |> suppressMessages() - + ## test that the three and ten day incidence matches the aggregated daily one ## except for first time window where we impose I = 1 on first day - + for(i in seq_len(floor(length(res_daily$I) / 3))[-1]){ expect_equal(sum(res_daily$I[i*3+(1:3)]), sum(res_three$I[i*3+(1:3)])) } - + for(i in seq_len(floor(length(res_daily$I) / 10))[-1]){ expect_equal(sum(res_daily$I[i*10+(1:10)]), sum(res_ten$I[i*10+(1:10)])) } - - ## test that weekly sliding R estimates are similar when using different + + ## test that weekly sliding R estimates are similar when using different ## temporal aggregations of data - + common_t_end_three <- intersect(res_weekly$R$t_end, res_three$R$t_end) common_t_end_ten <- intersect(res_weekly$R$t_end, res_ten$R$t_end) - + relative_error_three <- abs(res_weekly$R$`Mean(R)`[res_weekly$R$t_end %in% common_t_end_three] - res_three$R$`Mean(R)`[res_three$R$t_end %in% common_t_end_three]) / res_weekly$R$`Mean(R)`[res_weekly$R$t_end %in% common_t_end_three] - + relative_error_ten <- abs(res_weekly$R$`Mean(R)`[res_weekly$R$t_end %in% common_t_end_ten] - res_ten$R$`Mean(R)`[res_ten$R$t_end %in% common_t_end_ten]) / res_weekly$R$`Mean(R)`[res_weekly$R$t_end %in% common_t_end_ten] - + ## lot of variation early on so excluding first part expect_true(all(relative_error_three[-(1:20)] < 0.4)) # 0.4 arbitrarily small - expect_true(all(relative_error_ten[-(1:20)] < 0.5)) + expect_true(all(relative_error_ten[-(1:20)] < 0.5)) }) @@ -800,49 +797,49 @@ test_that("estimate_R_agg handles different incid input formats consistently", { method <- "parametric_si" config <- make_config(list(mean_si = mean_si, std_si = std_si)) - + # integer vector res_int <- estimate_R( incid = weekly_inc, - dt = 7L, - dt_out = 7L, - iter = 10L, - config = config, + dt = 7L, + dt_out = 7L, + iter = 10L, + config = config, method = method ) |> suppressWarnings() |> suppressMessages() - + # numeric vector res_numeric <- estimate_R( incid = as.numeric(weekly_inc), - dt = 7L, - dt_out = 7L, - iter = 10L, - config = config, + dt = 7L, + dt_out = 7L, + iter = 10L, + config = config, method = method ) |> suppressWarnings() |> suppressMessages() - + start_date <- as.Date("2020-01-01") - + # dataframe with I column weekly_inc_df <- data.frame(I = weekly_inc, dates = seq(start_date, by = "week", length.out = length(weekly_inc))) res_df <- estimate_R( incid = weekly_inc_df, - dt = 7L, - dt_out = 7L, - iter = 10L, - config = config, - method = method, + dt = 7L, + dt_out = 7L, + iter = 10L, + config = config, + method = method, date_convention = "start" ) |> suppressWarnings() |> suppressMessages() - + # incidence object inc_obj <- incidence::as.incidence(weekly_inc, dates = seq(start_date, by = "week", @@ -851,16 +848,16 @@ test_that("estimate_R_agg handles different incid input formats consistently", { res_inc_obj <- estimate_R( incid = inc_obj, - dt = 7L, - dt_out = 7L, - iter = 10L, - config = config, - method = method, + dt = 7L, + dt_out = 7L, + iter = 10L, + config = config, + method = method, date_convention = "start" ) |> suppressWarnings() |> suppressMessages() - + # incidence2 object data <- data.frame( I = weekly_inc, @@ -872,26 +869,26 @@ test_that("estimate_R_agg handles different incid input formats consistently", { res_inc2_obj <- estimate_R( incid = inc2_obj, - dt = 7L, - dt_out = 7L, - iter = 10L, - config = config, - method = method, + dt = 7L, + dt_out = 7L, + iter = 10L, + config = config, + method = method, date_convention = "start" ) |> suppressWarnings() |> suppressMessages() - + ## check it works with incidence2 without naming inc2_obj2 <- incidence2::incidence(data, date_index = "dates", counts = "I") res_inc2_obj2 <- estimate_R( incid = inc2_obj2, - dt = 7L, - dt_out = 7L, - iter = 10L, - config = config, - method = method, + dt = 7L, + dt_out = 7L, + iter = 10L, + config = config, + method = method, date_convention = "start" ) |> suppressWarnings() |> @@ -901,20 +898,20 @@ test_that("estimate_R_agg handles different incid input formats consistently", { drop_date_cols <- function(R) { R[, !names(R) %in% c("date_start", "date_end")] } - + # all formats should produce identical R estimates expect_equal(drop_date_cols(res_int$R), drop_date_cols(res_numeric$R)) expect_equal(drop_date_cols(res_int$R), drop_date_cols(res_df$R)) expect_equal(drop_date_cols(res_int$R), drop_date_cols(res_inc_obj$R)) expect_equal(drop_date_cols(res_int$R), drop_date_cols(res_inc2_obj$R)) expect_equal(drop_date_cols(res_int$R), drop_date_cols(res_inc2_obj2$R)) - + # check date columns present when dates supplied expect_true(all(c("date_start", "date_end") %in% names(res_df$R))) expect_true(all(c("date_start", "date_end") %in% names(res_inc_obj$R))) expect_true(all(c("date_start", "date_end") %in% names(res_inc2_obj$R))) expect_true(all(c("date_start", "date_end") %in% names(res_inc2_obj2$R))) - + # check they match each other expect_equal(res_df$R$date_start, res_inc_obj$R$date_start) expect_equal(res_df$R$date_end, res_inc_obj$R$date_end) @@ -936,11 +933,11 @@ test_that("t_start and t_end work when dt is used without specifying dt_out", { expect_no_error( res <- estimate_R( incid = weekly_inc, - dt = 7L, - iter = 10L, - config = config, + dt = 7L, + iter = 10L, + config = config, method = method - ) + ) ) |> suppressWarnings() |> suppressMessages() @@ -986,10 +983,10 @@ test_that("date_convention works with 'start' and 'end'", { res_start <- estimate_R( incid = weekly_inc_df, - dt = 7L, - iter = 10L, - config = config, - method = method, + dt = 7L, + iter = 10L, + config = config, + method = method, date_convention = "start" ) |> suppressWarnings() |> @@ -999,10 +996,10 @@ test_that("date_convention works with 'start' and 'end'", { res_end <- estimate_R( incid = weekly_inc_df, - dt = 7L, - iter = 10L, - config = config, - method = method, + dt = 7L, + iter = 10L, + config = config, + method = method, date_convention = "end" ) |> suppressWarnings() |> @@ -1025,9 +1022,9 @@ test_that("date_convention defaults to 'end'", { res_end <- estimate_R( incid = weekly_inc_df, - dt = 7L, - iter = 10L, - config = config, + dt = 7L, + iter = 10L, + config = config, method = method ) |> suppressWarnings() |> @@ -1053,10 +1050,10 @@ test_that("date_convention works with irregular aggregations", { res_start_irr <- estimate_R( incid = agg_inc_df, - dt = c(3L, 3L, 2L), - iter = 10L, - config = config, - method = method, + dt = c(3L, 3L, 2L), + iter = 10L, + config = config, + method = method, date_convention = "start" ) |> suppressWarnings() |> @@ -1067,10 +1064,10 @@ test_that("date_convention works with irregular aggregations", { res_end_irr <- estimate_R( incid = agg_inc_df, - dt = c(3L, 3L, 2L), - iter = 10L, - config = config, - method = method, + dt = c(3L, 3L, 2L), + iter = 10L, + config = config, + method = method, date_convention = "end" ) |> suppressWarnings() |> From 0f09caea25b9d6e7b833ae4699706aa159f99db6 Mon Sep 17 00:00:00 2001 From: Steffi LaZerte Date: Tue, 2 Jun 2026 16:31:25 -0500 Subject: [PATCH 18/31] Fix problem with snapshot differing on ubuntu? --- tests/testthat/test-wallinga_teunis.R | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/testthat/test-wallinga_teunis.R b/tests/testthat/test-wallinga_teunis.R index b5421d07..d1c991a5 100644 --- a/tests/testthat/test-wallinga_teunis.R +++ b/tests/testthat/test-wallinga_teunis.R @@ -271,7 +271,7 @@ test_that( test_that("Example 1 matches saved output", { data("Flu2009") - + set.seed(1) out <- wallinga_teunis(Flu2009$incidence, method = "non_parametric_si", config = list(t_start = 2:26, t_end = 8:32, si_distr = Flu2009$si_distr, From 876d0c44f12ffa07840009fefa55841ed89339b5 Mon Sep 17 00:00:00 2001 From: Steffi LaZerte Date: Tue, 2 Jun 2026 16:41:32 -0500 Subject: [PATCH 19/31] Address finicky tests with withr::with_seed? --- DESCRIPTION | 3 ++- tests/testthat/test-samplers.R | 14 ++++++++++---- tests/testthat/test-wallinga_teunis.R | 18 ++++++++++++------ 3 files changed, 24 insertions(+), 11 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index ff9754a9..1f22e19f 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -53,7 +53,8 @@ Suggests: projections, dplyr, readxl, - gghighlight + gghighlight, + withr License: GPL (>=2) LazyLoad: yes Encoding: UTF-8 diff --git a/tests/testthat/test-samplers.R b/tests/testthat/test-samplers.R index 5d295867..533d6ee8 100644 --- a/tests/testthat/test-samplers.R +++ b/tests/testthat/test-samplers.R @@ -702,9 +702,15 @@ test_that("estimate_advantage produces expected results (2 var, 2 loc, R_loc1 = priors <- default_priors() - x <- estimate_advantage( - incid, si_distr, priors, seed = 1, t_min = 2L - ) |> suppressMessages() + withr::with_seed(1, { + x <- estimate_advantage( + incid, + si_distr, + priors, + t_min = 2L + ) |> + suppressMessages() + }) ## R should be approx 1.1 for loc1 and 1.5 for loc2 expect_equal(mean(x$R[,1,], na.rm = TRUE), 1.1, tolerance = 0.5) @@ -712,7 +718,7 @@ test_that("estimate_advantage produces expected results (2 var, 2 loc, R_loc1 = expect_s3_class(x$diag[[1]], "gelman.diag") epi_snapshot_value(x) -}) + }) test_that("estimate_advantage faster with precompute (2 variants 3 locations)", { n_v <- 2 # 2 variants diff --git a/tests/testthat/test-wallinga_teunis.R b/tests/testthat/test-wallinga_teunis.R index d1c991a5..034eee37 100644 --- a/tests/testthat/test-wallinga_teunis.R +++ b/tests/testthat/test-wallinga_teunis.R @@ -271,11 +271,17 @@ test_that( test_that("Example 1 matches saved output", { data("Flu2009") - set.seed(1) - out <- wallinga_teunis(Flu2009$incidence, method = "non_parametric_si", - config = list(t_start = 2:26, t_end = 8:32, - si_distr = Flu2009$si_distr, - seed = 1, - n_sim = 50)) + withr::with_seed(1, { + out <- wallinga_teunis( + Flu2009$incidence, + method = "non_parametric_si", + config = list( + t_start = 2:26, + t_end = 8:32, + si_distr = Flu2009$si_distr, + n_sim = 50 + ) + ) + }) epi_snapshot_value(out, n = NULL) }) \ No newline at end of file From 1a6b25caaba3c59962efdaadc60f9cf9a4e898e8 Mon Sep 17 00:00:00 2001 From: Steffi LaZerte Date: Tue, 2 Jun 2026 16:42:52 -0500 Subject: [PATCH 20/31] Allow lower digits matches where problematic comparing macos --- R/test_utils.R | 4 ++-- tests/testthat/test-samplers.R | 16 +++++----------- 2 files changed, 7 insertions(+), 13 deletions(-) diff --git a/R/test_utils.R b/R/test_utils.R index a7711c4d..c3d42cac 100644 --- a/R/test_utils.R +++ b/R/test_utils.R @@ -47,7 +47,7 @@ epi_expect_doppelganger <- function(name, code, variant) { #' @param n Numeric. Number of samples to keep #' #' @noRd -epi_snapshot_value <- function(x, style = "json2", n = 5) { +epi_snapshot_value <- function(x, style = "json2", n = 5, digits = 8) { sample_x <- function(xx) { # Recurse if this is still a list @@ -60,7 +60,7 @@ epi_snapshot_value <- function(x, style = "json2", n = 5) { } # Round to avoid mismatches from truncated records - if(is.numeric(xx)) xx <- round(xx, digits = 8) + if(is.numeric(xx)) xx <- round(xx, digits = digits) xx } diff --git a/tests/testthat/test-samplers.R b/tests/testthat/test-samplers.R index 533d6ee8..4ca4e8ab 100644 --- a/tests/testthat/test-samplers.R +++ b/tests/testthat/test-samplers.R @@ -702,23 +702,17 @@ test_that("estimate_advantage produces expected results (2 var, 2 loc, R_loc1 = priors <- default_priors() - withr::with_seed(1, { - x <- estimate_advantage( - incid, - si_distr, - priors, - t_min = 2L - ) |> - suppressMessages() - }) + x <- estimate_advantage( + incid, si_distr, priors, seed = 1, t_min = 2L + ) |> suppressMessages() ## R should be approx 1.1 for loc1 and 1.5 for loc2 expect_equal(mean(x$R[,1,], na.rm = TRUE), 1.1, tolerance = 0.5) expect_equal(mean(x$R[,2,], na.rm = TRUE), 1.5, tolerance = 0.5) expect_s3_class(x$diag[[1]], "gelman.diag") - epi_snapshot_value(x) - }) + epi_snapshot_value(x, digits = 6) +}) test_that("estimate_advantage faster with precompute (2 variants 3 locations)", { n_v <- 2 # 2 variants From 4743d000188129b83b4b41e0a5cedf5e1d747a27 Mon Sep 17 00:00:00 2001 From: Steffi LaZerte Date: Tue, 2 Jun 2026 16:52:19 -0500 Subject: [PATCH 21/31] Update truncated samplers snaps --- tests/testthat/_snaps/samplers.md | 21077 +--------------------------- 1 file changed, 4 insertions(+), 21073 deletions(-) diff --git a/tests/testthat/_snaps/samplers.md b/tests/testthat/_snaps/samplers.md index 15e263b9..095972cd 100644 --- a/tests/testthat/_snaps/samplers.md +++ b/tests/testthat/_snaps/samplers.md @@ -1,20880 +1,3 @@ -# draw_epsilon produces expected results (2 variants, 4 locations) - - { - "type": "double", - "attributes": {}, - "value": [1.00245545, 0.99460201, 1.01794685, 1.02167213, 1.00642198] - } - -# draw_epsilon produces expected results (2 variants, 1 location) - - { - "type": "double", - "attributes": {}, - "value": [0.9934614, 0.97786211, 1.02441227, 1.03189037, 1.00136363] - } - -# draw_epsilon produces expected results (>2 variants, 4 locations) - - { - "type": "double", - "attributes": {}, - "value": [1.04495952, 1.01610473, 0.99460201, 1.01794685, 1.02167213] - } - -# draw_epsilon produces expected results (>2 variants, 1 location) - - { - "type": "double", - "attributes": {}, - "value": [1.07894037, 1.02071943, 0.97786211, 1.02441227, 1.03189037] - } - -# draw_epsilon seed - - { - "type": "double", - "attributes": {}, - "value": [0.99069615, 1.05387124, 0.99069615, 0.99069615, 1.05387124] - } - -# draw_R produces expected results (2 variants, 4 locations) - - { - "type": "list", - "attributes": {}, - "value": [ - { - "type": "double", - "attributes": {}, - "value": [0.89091945, 1.34883043, 0.84282641, 0.63244426, 0.79784422] - }, - { - "type": "double", - "attributes": {}, - "value": [0.93882838, 1.28206494, 1.20229884, 1.06496217, 1.01970474] - }, - { - "type": "double", - "attributes": {}, - "value": [1.51780022, 0.96531844, 1.10928708, 0.91372191, 1.12348027] - }, - { - "type": "double", - "attributes": {}, - "value": [0.70998505, 0.97325563, 0.73939673, 0.7963231, 0.75603737] - }, - { - "type": "double", - "attributes": {}, - "value": [1.12013827, 0.71479584, 1.02251284, 1.08188688, 0.87584597] - }, - { - "type": "double", - "attributes": {}, - "value": [1.16111275, 1.27466446, 1.05636154, 1.01782201, 1.07040421] - }, - { - "type": "double", - "attributes": {}, - "value": [1.02100017, 1.01730698, 0.85741344, 1.03036101, 1.2554566] - }, - { - "type": "double", - "attributes": {}, - "value": [0.88985681, 0.81206717, 1.30897476, 0.69429295, 1.13429026] - }, - { - "type": "double", - "attributes": {}, - "value": 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[0.79478208, 1.10663897, "NA", 1.01538894, 0.9282724] - }, - { - "type": "double", - "attributes": {}, - "value": [0.96560004, 1.02889871, "NA", 0.94017536, 0.97232767] - }, - { - "type": "double", - "attributes": {}, - "value": [0.86609244, 1.15712565, "NA", 1.25631155, 1.12771063] - }, - { - "type": "double", - "attributes": {}, - "value": [0.89175423, 1.20060782, "NA", 0.87250261, 1.05366459] - }, - { - "type": "double", - "attributes": {}, - "value": [1.14052718, 1.07034745, "NA", 0.96516233, 0.92273121] - }, - { - "type": "double", - "attributes": {}, - "value": [1.2445378, 1.00495494, "NA", 0.99041476, 1.05442047] - }, - { - "type": "double", - "attributes": {}, - "value": [1.08437983, 0.74101676, "NA", 0.78072374, 1.07612127] - }, - { - "type": "double", - "attributes": {}, - "value": [1.01025476, 0.99162084, "NA", 0.54821381, 1.25956182] - }, - { - "type": "double", - "attributes": {}, - "value": [1.12941415, 0.90783775, "NA", 1.3374749, 1.09404799] - }, - { - "type": "double", - "attributes": {}, - "value": [0.99881894, 0.99817224, "NA", 0.82130324, 1.16863346] - }, - { - "type": "double", - "attributes": {}, - "value": [0.94013565, 1.27208568, "NA", 0.88324384, 1.08683785] - }, - { - "type": "double", - "attributes": {}, - "value": [1.13451566, 1.05896078, "NA", 1.16876004, 1.29619505] - }, - { - "type": "double", - "attributes": {}, - "value": [0.76588056, 1.09722053, "NA", 1.13990189, 0.81662643] - }, - { - "type": "double", - "attributes": {}, - "value": [1.0905157, 0.88113502, "NA", 1.25209767, 1.35726578] - }, - { - "type": "double", - "attributes": {}, - "value": [1.11225502, 1.3845943, "NA", 0.8727771, 1.00567189] - }, - { - "type": "double", - "attributes": {}, - "value": [1.12352125, 0.8640765, "NA", 1.0133665, 0.91273461] - }, - { - "type": "double", - "attributes": {}, - "value": [0.90556869, 0.83614914, "NA", 0.86519185, 1.10498766] - }, - { - "type": "double", - "attributes": {}, - "value": [0.93963244, 1.00058337, "NA", 0.78898687, 1.09684123] - }, - { - "type": "double", - "attributes": {}, - "value": [1.06000246, 1.507868, "NA", 0.92126848, 0.91224541] - }, - { - "type": "double", - "attributes": {}, - "value": [0.73974104, 0.93479013, "NA", 0.80705609, 1.33850184] - }, - { - "type": "double", - "attributes": {}, - "value": [0.91517736, 0.80233604, "NA", 1.27991289, 1.18125782] - }, - { - "type": "double", - "attributes": {}, - "value": [1.10965574, 0.92493831, "NA", 1.17103215, 0.95978701] - }, - { - "type": "double", - "attributes": {}, - "value": [1.01740521, 1.02904147, "NA", 0.92854232, 1.30036428] - }, - { - "type": "double", - "attributes": {}, - "value": [0.91946023, 0.80818361, "NA", 0.78556448, 0.95481587] - }, - { - "type": "double", - "attributes": {}, - "value": [1.20900905, 0.72093426, "NA", 1.04173511, 1.15365228] - }, - { - "type": "double", - "attributes": {}, - "value": [0.90348275, 0.98870843, "NA", 1.04271437, 0.93767461] - }, - { - "type": "double", - "attributes": {}, - "value": [1.09559009, 0.78244914, "NA", 1.11080682, 1.15617673] - }, - { - "type": "double", - "attributes": {}, - "value": [1.14886757, 1.17899875, "NA", 1.23684212, 0.89023841] - }, - { - "type": "double", - "attributes": {}, - "value": [1.02844345, 1.19478467, "NA", 1.03340399, 1.23243001] - }, - { - "type": "double", - "attributes": {}, - "value": [1.13579812, 0.9917308, "NA", 1.15993767, 1.16250488] - }, - { - "type": "double", - "attributes": {}, - "value": [1.2588676, 0.7118642, "NA", 1.29412847, 0.85310883] - }, - { - "type": "double", - "attributes": {}, - "value": [1.31893307, 0.57937998, "NA", 1.15947139, 1.13185919] - }, - { - "type": "double", - "attributes": {}, - "value": [0.97258791, 0.79657417, "NA", 0.84376559, 0.81537669] - }, - { - "type": "double", - "attributes": {}, - "value": [0.98863114, 1.50492334, "NA", 0.97158708, 0.53322173] - }, - { - "type": "double", - "attributes": {}, - "value": [1.10959302, 1.29056936, "NA", 1.16916978, 1.0558244] - }, - { - "type": "double", - "attributes": {}, - "value": [1.12409144, 0.89143356, "NA", 0.79979966, 1.15099137] - }, - { - "type": "double", - "attributes": {}, - "value": [1.02085495, 0.93632586, "NA", 0.9606416, 0.88969393] - }, - { - "type": "double", - "attributes": {}, - "value": [1.13863551, 0.78641475, "NA", 0.8090353, 0.88078482] - }, - { - "type": "double", - "attributes": {}, - "value": [0.85123263, 1.14066843, "NA", 1.09225686, 0.78835378] - }, - { - "type": "double", - "attributes": {}, - "value": [1.08537943, 1.28073226, "NA", 1.15555182, 0.99160021] - }, - { - "type": "double", - "attributes": {}, - "value": [0.77267291, 1.10178656, "NA", 0.91976444, 1.16469489] - }, - { - "type": "double", - "attributes": {}, - "value": [0.96696617, 1.05641401, "NA", 1.02885027, 0.66111679] - }, - { - "type": "double", - "attributes": {}, - "value": [1.13398927, 1.59530754, "NA", 0.86891858, 1.056934] - }, - { - "type": "double", - "attributes": {}, - "value": [0.92157042, 1.13410083, "NA", 0.80318551, 0.98924614] - }, - { - "type": "double", - "attributes": {}, - "value": [0.95675015, 1.04309683, "NA", 0.94886544, 0.67745694] - }, - { - "type": "double", - "attributes": {}, - "value": [1.00391168, 0.91563638, "NA", 1.23256479, 0.64245106] - }, - { - "type": "double", - "attributes": {}, - "value": [1.47712468, 0.97637913, "NA", 1.09422904, 1.28906597] - }, - { - "type": "double", - "attributes": {}, - "value": [0.96136238, 0.77753442, "NA", 1.06067246, 1.02184494] - }, - { - "type": "double", - "attributes": {}, - "value": [1.25860762, 0.8676425, "NA", 0.98200475, 1.20858477] - }, - { - "type": "double", - "attributes": {}, - "value": [0.80740284, 1.01262668, "NA", 0.82399758, 0.94577053] - }, - { - "type": "double", - "attributes": {}, - "value": [1.0032681, 1.05318223, "NA", 0.89663226, 1.23948641] - }, - { - "type": "double", - "attributes": {}, - "value": [0.82488371, 1.10459848, "NA", 1.10850732, 1.43148034] - }, - { - "type": "double", - "attributes": {}, - "value": [0.86898183, 0.97199921, "NA", 1.04986135, 1.08839753] - }, - { - "type": "double", - "attributes": {}, - "value": [0.94476406, 0.94170392, "NA", 0.88597375, 0.8051878] - }, - { - "type": "double", - "attributes": {}, - "value": [0.79430463, 0.92679562, "NA", 1.23787713, 1.05099903] - }, - { - "type": "double", - "attributes": {}, - "value": [0.87632896, 0.77279809, "NA", 0.82070522, 1.07152046] - }, - { - "type": "double", - "attributes": {}, - "value": [0.94515018, 0.90440306, "NA", 1.01917537, 0.98912257] - }, - { - "type": "double", - "attributes": {}, - "value": [0.97582004, 1.13222077, "NA", 1.00754749, 0.81526977] - }, - { - "type": "double", - "attributes": {}, - "value": [1.04252951, 0.9339682, "NA", 1.13552508, 0.58450091] - }, - { - "type": "double", - "attributes": {}, - "value": [0.93801645, 1.46404044, "NA", 0.73292771, 1.17285559] - }, - { - "type": "double", - "attributes": {}, - "value": [0.6541665, 0.87389629, "NA", 0.98704296, 0.89822526] - }, - { - "type": "double", - "attributes": {}, - "value": [0.68478309, 1.19514308, "NA", 1.19565123, 1.12823353] - } - ] - } - -# draw_R seed - - { - "type": "double", - "attributes": {}, - "value": [0.94404007, 1.12731997, "NA", 0.9538139, 1.32909981] - } - -# estimate_advantage produces expected results (2 variants 3 locations) - - { - "type": "list", - "attributes": { - "names": { - "type": "character", - "attributes": {}, - "value": ["epsilon", "R", "convergence", "diag"] - } - }, - "value": [ - { - "type": "double", - "attributes": {}, - "value": [1.04125274, 1.04420411, 0.98760227, 0.9614608, 0.98635696] - }, - { - "type": "double", - "attributes": {}, - "value": [1.29132805, 0.99766874, 0.92202266, 0.97147169, 1.24692569] - }, - { - "type": "logical", - "attributes": {}, - "value": [true] - }, - { - "type": "list", - "attributes": {}, - "value": [ - { - "type": "list", - "attributes": { - "names": { - "type": "character", - "attributes": {}, - "value": ["psrf", "mpsrf"] - } - }, - "value": [ - { - "type": "double", - "attributes": { - "dim": { - "type": "integer", - "attributes": {}, - "value": [1, 2] - }, - "dimnames": { - "type": "list", - "attributes": {}, - "value": [ - { - "type": "NULL" - }, - { - "type": "character", - "attributes": {}, - "value": ["Point est.", "Upper C.I."] - } - ] - } - }, - "value": [0.99906146, 1.0075281] - }, - { - "type": "NULL" - } - ] - } - ] - } - ] - } - -# estimate_advantage produces expected results (2 variants 1 location) - - { - "type": "list", - "attributes": { - "names": { - "type": "character", - "attributes": {}, - "value": ["epsilon", "R", "convergence", "diag"] - } - }, - "value": [ - { - "type": "double", - "attributes": {}, - "value": [1.04271486, 0.95213998, 0.9775455, 1.00751841, 1.06043541] - }, - { - "type": "double", - "attributes": {}, - "value": [0.85713661, 0.99504745, 1.59755113, 0.94067038, 1.5918051] - }, - { - "type": "logical", - "attributes": {}, - "value": [true] - }, - { - "type": "list", - "attributes": {}, - "value": [ - { - "type": "list", - "attributes": { - "names": { - "type": "character", - "attributes": {}, - "value": ["psrf", "mpsrf"] - } - }, - "value": [ - { - "type": "double", - "attributes": { - "dim": { - "type": "integer", - "attributes": {}, - "value": [1, 2] - }, - "dimnames": { - "type": "list", - "attributes": {}, - "value": [ - { - "type": "NULL" - }, - { - "type": "character", - "attributes": {}, - "value": ["Point est.", "Upper C.I."] - } - ] - } - }, - "value": [0.99676223, 1.04637177] - }, - { - "type": "NULL" - } - ] - } - ] - } - ] - } - -# estimate_advantage produces expected results (>2 variants 4 locs) - - { - "type": "list", - "attributes": { - "names": { - "type": "character", - "attributes": {}, - "value": ["epsilon", "R", "convergence", "diag"] - } - }, - "value": [ - { - "type": "double", - "attributes": {}, - "value": [0.97699048, 0.97927712, 0.96709735, 1.0127237, 0.9741251] - }, - { - "type": "double", - "attributes": {}, - "value": [1.26994619, 1.11496927, 1.22484032, 0.78260032, 1.02935147] - }, - { - "type": "logical", - "attributes": {}, - "value": [true, true] - }, - { - "type": "list", - "attributes": {}, - "value": [ - { - "type": "list", - "attributes": { - "names": { - "type": "character", - "attributes": {}, - "value": ["psrf", "mpsrf"] - } - }, - "value": [ - { - "type": "double", - "attributes": { - "dim": { - "type": "integer", - "attributes": {}, - "value": [1, 2] - }, - "dimnames": { - "type": "list", - "attributes": {}, - "value": [ - { - "type": "NULL" - }, - { - "type": "character", - "attributes": {}, - "value": ["Point est.", "Upper C.I."] - } - ] - } - }, - "value": [0.98478887, 0.99198357] - }, - { - "type": "NULL" - } - ] - }, - { - "type": "list", - "attributes": { - "names": { - "type": "character", - "attributes": {}, - "value": ["psrf", "mpsrf"] - } - }, - "value": [ - { - "type": "double", - "attributes": { - "dim": { - "type": "integer", - "attributes": {}, - "value": [1, 2] - }, - "dimnames": { - "type": "list", - "attributes": {}, - "value": [ - { - "type": "NULL" - }, - { - "type": "character", - "attributes": {}, - "value": ["Point est.", "Upper C.I."] - } - ] - } - }, - "value": [0.99367021, 1.00567634] - }, - { - "type": "NULL" - } - ] - } - ] - } - ] - } - -# estimate_advantage produces expected results (>2 variants 1 loc) - - { - "type": "list", - "attributes": { - "names": { - "type": "character", - "attributes": {}, - "value": ["epsilon", "R", "convergence", "diag"] - } - }, - "value": [ - { - "type": "double", - "attributes": {}, - "value": [1.00070896, 1.01226561, 1.00211123, 1.03963539, 1.04309282] - }, - { - "type": "double", - "attributes": {}, - "value": [0.88871014, 0.88146824, 1.1264103, 0.96660343, 0.77642176] - }, - { - "type": "logical", - "attributes": {}, - "value": [true, false] - }, - { - "type": "list", - "attributes": {}, - "value": [ - { - "type": "list", - "attributes": { - "names": { - "type": "character", - "attributes": {}, - "value": ["psrf", "mpsrf"] - } - }, - "value": [ - { - "type": "double", - "attributes": { - "dim": { - "type": "integer", - "attributes": {}, - "value": [1, 2] - }, - "dimnames": { - "type": "list", - "attributes": {}, - "value": [ - { - "type": "NULL" - }, - { - "type": "character", - "attributes": {}, - "value": ["Point est.", "Upper C.I."] - } - ] - } - }, - "value": [1.06626227, 1.09796743] - }, - { - "type": "NULL" - } - ] - }, - { - "type": "list", - "attributes": { - "names": { - "type": "character", - "attributes": {}, - "value": ["psrf", "mpsrf"] - } - }, - "value": [ - { - "type": "double", - "attributes": { - "dim": { - "type": "integer", - "attributes": {}, - "value": [1, 2] - }, - "dimnames": { - "type": "list", - "attributes": {}, - "value": [ - { - "type": "NULL" - }, - { - "type": "character", - "attributes": {}, - "value": ["Point est.", "Upper C.I."] - } - ] - } - }, - "value": [1.08588305, 1.2273234] - }, - { - "type": "NULL" - } - ] - } - ] - } - ] - } - -# process_I_multivariant works as expected - - { - "type": "list", - "attributes": { - "names": { - "type": "character", - "attributes": {}, - "value": ["local", "imported"] - } - }, - "value": [ - { - "type": "double", - "attributes": {}, - "value": [10, 10, 10, 10, 10] - }, - { - "type": "double", - "attributes": {}, - "value": [0, 0, 0, 0, 0] - } - ] - } - -# estimate_advantage produces expected results (>2var, 1loc, imports) - - { - "type": "list", - "attributes": { - "names": { - "type": "character", - "attributes": {}, - "value": ["epsilon", "R", "convergence", "diag"] - } - }, - "value": [ - { - "type": "double", - "attributes": {}, - "value": [0.00042594, 0.00086427, 0.0002672, 0.00318991, 0.00002992] - }, - { - "type": "double", - "attributes": {}, - "value": [0.48364105, 0.93722817, 0.70361866, 0.84555007, 0.38715794] - }, - { - "type": "logical", - "attributes": {}, - "value": [true, false] - }, - { - "type": "list", - "attributes": {}, - "value": [ - { - "type": "list", - "attributes": { - "names": { - "type": "character", - "attributes": {}, - "value": ["psrf", "mpsrf"] - } - }, - "value": [ - { - "type": "double", - "attributes": { - "dim": { - "type": "integer", - "attributes": {}, - "value": [1, 2] - }, - "dimnames": { - "type": "list", - "attributes": {}, - "value": [ - { - "type": "NULL" - }, - { - "type": "character", - "attributes": {}, - "value": ["Point est.", "Upper C.I."] - } - ] - } - }, - "value": [1.02180146, 1.04177601] - }, - { - "type": "NULL" - } - ] - }, - { - "type": "list", - "attributes": { - "names": { - "type": "character", - "attributes": {}, - "value": ["psrf", "mpsrf"] - } - }, - "value": [ - { - "type": "double", - "attributes": { - "dim": { - "type": "integer", - "attributes": {}, - "value": [1, 2] - }, - "dimnames": { - "type": "list", - "attributes": {}, - "value": [ - { - "type": "NULL" - }, - { - "type": "character", - "attributes": {}, - "value": ["Point est.", "Upper C.I."] - } - ] - } - }, - "value": [1.30138256, 2.76090238] - }, - { - "type": "NULL" - } - ] - } - ] - } - ] - } - -# estimate_advantage produces expected results (>2var, 4loc, imports) - - { - "type": "list", - "attributes": { - "names": { - "type": "character", - "attributes": {}, - "value": ["epsilon", "R", "convergence", "diag"] - } - }, - "value": [ - { - "type": "double", - "attributes": {}, - "value": [6.8e-06, 0.00010827, 0.00009129, 0.0000513, 0.00009835] - }, - { - "type": "double", - "attributes": {}, - "value": [1.11202284, 1.62377233, 0.97466746, 1.4155541, 0.97185051] - }, - { - "type": "logical", - "attributes": {}, - "value": [false, true] - }, - { - "type": "list", - "attributes": {}, - "value": [ - { - "type": "list", - "attributes": { - "names": { - "type": "character", - "attributes": {}, - "value": ["psrf", "mpsrf"] - } - }, - "value": [ - { - "type": "double", - "attributes": { - "dim": { - "type": "integer", - "attributes": {}, - "value": [1, 2] - }, - "dimnames": { - "type": "list", - "attributes": {}, - "value": [ - { - "type": "NULL" - }, - { - "type": "character", - "attributes": {}, - "value": ["Point est.", "Upper C.I."] - } - ] - } - }, - "value": [1.08057539, 1.25813639] - }, - { - "type": "NULL" - } - ] - }, - { - "type": "list", - "attributes": { - "names": { - "type": "character", - "attributes": {}, - "value": ["psrf", "mpsrf"] - } - }, - "value": [ - { - "type": "double", - "attributes": { - "dim": { - "type": "integer", - "attributes": {}, - "value": [1, 2] - }, - "dimnames": { - "type": "list", - "attributes": {}, - "value": [ - { - "type": "NULL" - }, - { - "type": "character", - "attributes": {}, - "value": ["Point est.", "Upper C.I."] - } - ] - } - }, - "value": [0.99512604, 1.00472554] - }, - { - "type": "NULL" - } - ] - } - ] - } - ] - } - -# estimate_advantage produces expected results (2var, 1loc, imports) - - { - "type": "list", - "attributes": { - "names": { - "type": "character", - "attributes": {}, - "value": ["epsilon", "R", "convergence", "diag"] - } - }, - "value": [ - { - "type": "double", - "attributes": {}, - "value": [0.00158209, 0.00021575, 0.0001383, 0.00009621, 0.00039953] - }, - { - "type": "double", - "attributes": {}, - "value": [1.31434356, 0.6535476, 0.57880618, 1.11860124, 1.20305732] - }, - { - "type": "logical", - "attributes": {}, - "value": [true] - }, - { - "type": "list", - "attributes": {}, - "value": [ - { - "type": "list", - "attributes": { - "names": { - "type": "character", - "attributes": {}, - "value": ["psrf", "mpsrf"] - } - }, - "value": [ - { - "type": "double", - "attributes": { - "dim": { - "type": "integer", - "attributes": {}, - "value": [1, 2] - }, - "dimnames": { - "type": "list", - "attributes": {}, - "value": [ - { - "type": "NULL" - }, - { - "type": "character", - "attributes": {}, - "value": ["Point est.", "Upper C.I."] - } - ] - } - }, - "value": [0.99088731, 0.99936699] - }, - { - "type": "NULL" - } - ] - } - ] - } - ] - } - -# estimate_advantage produces expected results (2var, 4loc, imports) - - { - "type": "list", - "attributes": { - "names": { - "type": "character", - "attributes": {}, - "value": ["epsilon", "R", "convergence", "diag"] - } - }, - "value": [ - { - "type": "double", - "attributes": {}, - "value": [0.0003932, 0.00038339, 0.00005313, 0.00002397, 0.00025155] - }, - { - "type": "double", - "attributes": {}, - "value": [1.02125246, 1.53469409, 1.14810298, 1.46667941, 0.92564963] - }, - { - "type": "logical", - "attributes": {}, - "value": [true] - }, - { - "type": "list", - "attributes": {}, - "value": [ - { - "type": "list", - "attributes": { - "names": { - "type": "character", - "attributes": {}, - "value": ["psrf", "mpsrf"] - } - }, - "value": [ - { - "type": "double", - "attributes": { - "dim": { - "type": "integer", - "attributes": {}, - "value": [1, 2] - }, - "dimnames": { - "type": "list", - "attributes": {}, - "value": [ - { - "type": "NULL" - }, - { - "type": "character", - "attributes": {}, - "value": ["Point est.", "Upper C.I."] - } - ] - } - }, - "value": [1.03628412, 1.03693989] - }, - { - "type": "NULL" - } - ] - } - ] - } - ] - } - # estimate_advantage produces expected results (2 var, 2 loc, R_loc1 = 1.1, R_loc2 = 1.5) { @@ -20890,169 +13,17 @@ { "type": "double", "attributes": {}, - "value": [1.50000311, 1.49999275, 1.49999859, 1.50003524, 1.50000421] - }, - { - "type": "double", - "attributes": {}, - "value": ["NA", 1.49999502, 1.00473161, 1.49971186, 1.1000197] - }, - { - "type": "logical", - "attributes": {}, - "value": [false] - }, - { - "type": "list", - "attributes": {}, - "value": [ - { - "type": "list", - "attributes": { - "names": { - "type": "character", - "attributes": {}, - "value": ["psrf", "mpsrf"] - } - }, - "value": [ - { - "type": "double", - "attributes": { - "dim": { - "type": "integer", - "attributes": {}, - "value": [1, 2] - }, - "dimnames": { - "type": "list", - "attributes": {}, - "value": [ - { - "type": "NULL" - }, - { - "type": "character", - "attributes": {}, - "value": ["Point est.", "Upper C.I."] - } - ] - } - }, - "value": [1.01872211, 1.13081299] - }, - { - "type": "NULL" - } - ] - } - ] - } - ] - } - -# estimate_advantage uses the correct t_min - - { - "type": "list", - "attributes": { - "names": { - "type": "character", - "attributes": {}, - "value": ["epsilon", "R", "convergence", "diag"] - } - }, - "value": [ - { - "type": "double", - "attributes": {}, - "value": [1.00471817, 1.03107137, 1.02035086, 1.00506093, 1.00732389] - }, - { - "type": "double", - "attributes": {}, - "value": [0.91800364, 0.85016542, 0.71860597, 1.07449868, 1.30864555] - }, - { - "type": "logical", - "attributes": {}, - "value": [false] - }, - { - "type": "list", - "attributes": {}, - "value": [ - { - "type": "list", - "attributes": { - "names": { - "type": "character", - "attributes": {}, - "value": ["psrf", "mpsrf"] - } - }, - "value": [ - { - "type": "double", - "attributes": { - "dim": { - "type": "integer", - "attributes": {}, - "value": [1, 2] - }, - "dimnames": { - "type": "list", - "attributes": {}, - "value": [ - { - "type": "NULL" - }, - { - "type": "character", - "attributes": {}, - "value": ["Point est.", "Upper C.I."] - } - ] - } - }, - "value": [1.02097152, 1.11993594] - }, - { - "type": "NULL" - } - ] - } - ] - } - ] - } - -# estimate_advantage convergence checks work with >2 variants - - { - "type": "list", - "attributes": { - "names": { - "type": "character", - "attributes": {}, - "value": ["epsilon", "R", "convergence", "diag"] - } - }, - "value": [ - { - "type": "double", - "attributes": {}, - "value": [0.99403141, 1.0081723, 1.03317077, 1.00100681, 0.97877066] + "value": [1.500036, 1.500023, 1.500019, 1.50001, 1.500015] }, { "type": "double", "attributes": {}, - "value": ["NA", 0.95176321, 1.13296226, 1.02441156, 1.44535094] + "value": ["NA", 1.500005, 1.013014, 1.499871, 1.10004] }, { "type": "logical", "attributes": {}, - "value": [false, true] + "value": [true] }, { "type": "list", @@ -21091,47 +62,7 @@ ] } }, - "value": [1.27871678, 1.98232008] - }, - { - "type": "NULL" - } - ] - }, - { - "type": "list", - "attributes": { - "names": { - "type": "character", - "attributes": {}, - "value": ["psrf", "mpsrf"] - } - }, - "value": [ - { - "type": "double", - "attributes": { - "dim": { - "type": "integer", - "attributes": {}, - "value": [1, 2] - }, - "dimnames": { - "type": "list", - "attributes": {}, - "value": [ - { - "type": "NULL" - }, - { - "type": "character", - "attributes": {}, - "value": ["Point est.", "Upper C.I."] - } - ] - } - }, - "value": [1.04497671, 1.07304128] + "value": [0.997933, 1.056718] }, { "type": "NULL" From 13211e3f9e7c865dd8ed79a267faa933a298d90a Mon Sep 17 00:00:00 2001 From: Steffi LaZerte Date: Tue, 2 Jun 2026 16:56:15 -0500 Subject: [PATCH 22/31] Skip on mac --- tests/testthat/test-wallinga_teunis.R | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/tests/testthat/test-wallinga_teunis.R b/tests/testthat/test-wallinga_teunis.R index 034eee37..e9244757 100644 --- a/tests/testthat/test-wallinga_teunis.R +++ b/tests/testthat/test-wallinga_teunis.R @@ -270,9 +270,11 @@ test_that( test_that("Example 1 matches saved output", { + skip_on_os(os = "mac") # Consistently different from the other OS + data("Flu2009") withr::with_seed(1, { - out <- wallinga_teunis( + out1 <- wallinga_teunis( Flu2009$incidence, method = "non_parametric_si", config = list( From e24eb36d50189847ad0d63b8d3c683d7263988f8 Mon Sep 17 00:00:00 2001 From: Steffi LaZerte Date: Tue, 2 Jun 2026 17:11:33 -0500 Subject: [PATCH 23/31] Skip on mac where cannot compare randomness --- DESCRIPTION | 3 +-- tests/testthat/test-wallinga_teunis.R | 24 ++++++++++++------------ 2 files changed, 13 insertions(+), 14 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index 1f22e19f..ff9754a9 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -53,8 +53,7 @@ Suggests: projections, dplyr, readxl, - gghighlight, - withr + gghighlight License: GPL (>=2) LazyLoad: yes Encoding: UTF-8 diff --git a/tests/testthat/test-wallinga_teunis.R b/tests/testthat/test-wallinga_teunis.R index e9244757..a332bc13 100644 --- a/tests/testthat/test-wallinga_teunis.R +++ b/tests/testthat/test-wallinga_teunis.R @@ -271,19 +271,19 @@ test_that( test_that("Example 1 matches saved output", { skip_on_os(os = "mac") # Consistently different from the other OS - + data("Flu2009") - withr::with_seed(1, { - out1 <- wallinga_teunis( - Flu2009$incidence, - method = "non_parametric_si", - config = list( - t_start = 2:26, - t_end = 8:32, - si_distr = Flu2009$si_distr, - n_sim = 50 - ) + set.seed(1) + out <- wallinga_teunis( + Flu2009$incidence, + method = "non_parametric_si", + config = list( + t_start = 2:26, + t_end = 8:32, + si_distr = Flu2009$si_distr, + n_sim = 50 ) - }) + ) + epi_snapshot_value(out, n = NULL) }) \ No newline at end of file From 51c537a7770adac93a881f2f55cfdd59bc8ca981 Mon Sep 17 00:00:00 2001 From: Steffi LaZerte Date: Tue, 2 Jun 2026 17:11:48 -0500 Subject: [PATCH 24/31] set seed early to fix samplers? --- tests/testthat/_snaps/samplers.md | 8 ++++---- tests/testthat/test-samplers.R | 3 ++- 2 files changed, 6 insertions(+), 5 deletions(-) diff --git a/tests/testthat/_snaps/samplers.md b/tests/testthat/_snaps/samplers.md index 095972cd..f450e7cc 100644 --- a/tests/testthat/_snaps/samplers.md +++ b/tests/testthat/_snaps/samplers.md @@ -13,17 +13,17 @@ { "type": "double", "attributes": {}, - "value": [1.500036, 1.500023, 1.500019, 1.50001, 1.500015] + "value": [1.50003294, 1.49998546, 1.49999875, 1.49999887, 1.50000615] }, { "type": "double", "attributes": {}, - "value": ["NA", 1.500005, 1.013014, 1.499871, 1.10004] + "value": ["NA", 1.49997391, 1.06214731, 1.49989375, 1.09988289] }, { "type": "logical", "attributes": {}, - "value": [true] + "value": [false] }, { "type": "list", @@ -62,7 +62,7 @@ ] } }, - "value": [0.997933, 1.056718] + "value": [1.05795751, 1.25591529] }, { "type": "NULL" diff --git a/tests/testthat/test-samplers.R b/tests/testthat/test-samplers.R index 4ca4e8ab..1501921d 100644 --- a/tests/testthat/test-samplers.R +++ b/tests/testthat/test-samplers.R @@ -652,6 +652,7 @@ test_that("estimate_advantage produces expected results (2var, 4loc, imports)", test_that("estimate_advantage produces expected results (2 var, 2 loc, R_loc1 = 1.1, R_loc2 = 1.5)", { skip_if_not_installed("projections") + set.seed(1) n_v <- 2 # 2 variants n_loc <- 2 # 2 locations T <- 100 # 100 time steps @@ -711,7 +712,7 @@ test_that("estimate_advantage produces expected results (2 var, 2 loc, R_loc1 = expect_equal(mean(x$R[,2,], na.rm = TRUE), 1.5, tolerance = 0.5) expect_s3_class(x$diag[[1]], "gelman.diag") - epi_snapshot_value(x, digits = 6) + epi_snapshot_value(x) }) test_that("estimate_advantage faster with precompute (2 variants 3 locations)", { From b97e752a530b2f074adefee8a7e7b805926561dd Mon Sep 17 00:00:00 2001 From: Steffi LaZerte Date: Wed, 3 Jun 2026 13:55:15 -0500 Subject: [PATCH 25/31] Add missing samplers snaps --- tests/testthat/_snaps/samplers.md | 21069 ++++++++++++++++++++++++++++ 1 file changed, 21069 insertions(+) diff --git a/tests/testthat/_snaps/samplers.md b/tests/testthat/_snaps/samplers.md index f450e7cc..13fd64e3 100644 --- a/tests/testthat/_snaps/samplers.md +++ b/tests/testthat/_snaps/samplers.md @@ -1,3 +1,20880 @@ +# draw_epsilon produces expected results (2 variants, 4 locations) + + { + "type": "double", + "attributes": {}, + "value": [1.00245545, 0.99460201, 1.01794685, 1.02167213, 1.00642198] + } + +# draw_epsilon produces expected results (2 variants, 1 location) + + { + "type": "double", + "attributes": {}, + "value": [0.9934614, 0.97786211, 1.02441227, 1.03189037, 1.00136363] + } + +# draw_epsilon produces expected results (>2 variants, 4 locations) + + { + "type": "double", + "attributes": {}, + "value": [1.04495952, 1.01610473, 0.99460201, 1.01794685, 1.02167213] + } + +# draw_epsilon produces expected results (>2 variants, 1 location) + + { + "type": "double", + "attributes": {}, + "value": [1.07894037, 1.02071943, 0.97786211, 1.02441227, 1.03189037] + } + +# draw_epsilon seed + + { + "type": "double", + "attributes": {}, + "value": [0.99069615, 1.05387124, 0.99069615, 0.99069615, 1.05387124] + } + +# draw_R produces expected results (2 variants, 4 locations) + + { + "type": "list", + "attributes": {}, + "value": [ + { + "type": "double", + "attributes": {}, + "value": [0.89091945, 1.34883043, 0.84282641, 0.63244426, 0.79784422] + }, + { + "type": "double", + "attributes": {}, + "value": [0.93882838, 1.28206494, 1.20229884, 1.06496217, 1.01970474] + }, + { + "type": "double", + "attributes": {}, + "value": [1.51780022, 0.96531844, 1.10928708, 0.91372191, 1.12348027] + }, + { + "type": "double", + "attributes": {}, + "value": [0.70998505, 0.97325563, 0.73939673, 0.7963231, 0.75603737] + }, + { + "type": "double", + "attributes": {}, + "value": [1.12013827, 0.71479584, 1.02251284, 1.08188688, 0.87584597] + }, + { + "type": "double", + "attributes": {}, + "value": [1.16111275, 1.27466446, 1.05636154, 1.01782201, 1.07040421] + }, + { + "type": "double", + "attributes": {}, + "value": [1.02100017, 1.01730698, 0.85741344, 1.03036101, 1.2554566] + }, + { + "type": "double", + "attributes": {}, + "value": [0.88985681, 0.81206717, 1.30897476, 0.69429295, 1.13429026] + }, + { + "type": "double", + "attributes": {}, + "value": [1.08282103, 1.04907781, 1.22490253, 0.96461857, 0.69119988] + }, + { + "type": "double", + "attributes": {}, + "value": [0.86576206, 0.83780528, 0.64335045, 1.38616205, 0.97889898] + }, + { + "type": "double", + "attributes": {}, + "value": [1.13819508, 1.20735043, 0.99659683, 0.87374124, 1.18867275] + }, + { + "type": "double", + "attributes": {}, + "value": [0.83323897, 1.42889819, 0.85823747, 1.07235653, 1.04590616] + }, + { + "type": "double", + "attributes": {}, + "value": [1.29635374, 1.12101089, 1.18691419, 1.0382293, 1.27289693] + }, + { + "type": "double", + "attributes": {}, + "value": [0.98076798, 0.77386533, 1.22584739, 0.76950829, 0.95339691] + }, + { + "type": "double", + "attributes": {}, + "value": [0.87212613, 1.19741481, 0.92025217, 0.97237525, 0.77017054] + }, + { + "type": "double", + "attributes": {}, + "value": [0.80727529, 0.94125709, 0.93932224, 0.71044523, 1.23746823] + }, + { + "type": "double", + "attributes": {}, + "value": [0.83986139, 1.01321353, 0.93754198, 0.93613192, 1.10656284] + }, + { + "type": "double", + "attributes": {}, + "value": [1.07942438, 0.66746761, 0.82618577, 1.38306003, 1.09706955] + }, + { + "type": "double", + "attributes": {}, + "value": [0.75007472, 0.64960131, 0.94818967, 0.97716213, 0.94354994] + }, + { + "type": "double", + "attributes": {}, + "value": [1.09122148, 0.8828517, 0.663261, 1.12538318, 1.35521784] + }, + { + "type": "double", + "attributes": {}, + "value": [1.11494018, 1.16762835, 1.08405126, 1.16689123, 0.92438497] + }, + { + "type": "double", + "attributes": {}, + "value": [1.22878436, 0.574058, 1.4568147, 0.82225576, 1.15292728] + }, + { + "type": "double", + "attributes": {}, + "value": [0.71666175, 1.52995935, 1.08780354, 0.99350373, 0.79184497] + }, + { + "type": "double", + "attributes": {}, + "value": [1.32573182, 0.91989451, 0.79045036, 0.9273953, 1.06739555] + }, + { + "type": "double", + "attributes": {}, + "value": [0.90475722, 1.00785529, 1.01613831, 1.22657931, 0.91039907] + }, + { + "type": "double", + "attributes": {}, + "value": [0.81910966, 1.70059554, 0.74496206, 1.27564324, 0.95731678] + }, + { + "type": "double", + "attributes": {}, + "value": [1.12463522, 0.96964725, 1.0021263, 1.13632833, 1.15262033] + }, + { + "type": "double", + "attributes": {}, + "value": [0.97900311, 1.12628755, 1.10624509, 0.89649961, 0.85704511] + }, + { + "type": "double", + "attributes": {}, + "value": [0.91599872, 1.01480766, 0.54231891, 0.9887093, 0.64155309] + }, + { + "type": "double", + "attributes": {}, + "value": [1.33001224, 1.29082544, 1.2357217, 0.90857636, 0.68105302] + }, + { + "type": "double", + "attributes": {}, + "value": [0.4707721, 1.13834795, 0.96705827, 1.04817673, 0.72537195] + }, + { + "type": "double", + "attributes": {}, + "value": [1.09782603, 1.16164092, 0.96897135, 0.6773599, 1.31171191] + }, + { + "type": "double", + "attributes": {}, + "value": [0.73001317, 1.16410597, 1.08870787, 1.00124622, 0.69428161] + }, + { + "type": "double", + "attributes": {}, + "value": [1.19298319, 1.07124194, 0.82931512, 1.23827239, 0.82378689] + }, + { + "type": "double", + "attributes": {}, + "value": [1.10850058, 1.03975878, 1.10676956, 0.9404899, 1.01862534] + }, + { + "type": "double", + "attributes": {}, + "value": [1.15274386, 1.03878194, 1.28664339, 0.59213056, 0.68538475] + }, + { + "type": "double", + "attributes": {}, + "value": [1.28153432, 1.23481788, 0.75500501, 0.60240474, 0.92537742] + }, + { + "type": "double", + "attributes": {}, + "value": [0.93356616, 1.2197564, 1.23843669, 1.00729515, 0.92783081] + }, + { + "type": "double", + "attributes": {}, + "value": [1.14274999, 0.91282598, 1.10273146, 1.03302939, 0.69934721] + }, + { + "type": "double", + "attributes": {}, + "value": [1.04984112, 0.82271246, 0.56394325, 1.15038852, 1.02900994] + }, + { + "type": "double", + "attributes": {}, + "value": [0.90608456, 0.71545452, 0.84418857, 1.38906309, 0.56205113] + }, + { + "type": "double", + "attributes": {}, + "value": [0.76363474, 0.83962723, 1.23527019, 0.93342961, 0.85229627] + }, + { + "type": "double", + "attributes": {}, + "value": [0.46882717, 0.73537373, 1.01978812, 1.19000721, 1.19634568] + }, + { + "type": "double", + "attributes": {}, + "value": [0.9186468, 1.112969, 0.86596867, 1.0384154, 1.10826589] + }, + { + "type": "double", + "attributes": {}, + "value": [1.17537504, 0.93199373, 0.62003324, 1.19681086, 0.81437807] + }, + { + "type": "double", + "attributes": {}, + "value": [1.07907572, 1.02832131, 0.80177703, 0.89150181, 1.19829607] + }, + { + "type": "double", + "attributes": {}, + "value": [1.11186811, 0.84169123, 1.21873019, 1.04091903, 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"attributes": {}, + "value": [0.93801645, 1.46404044, "NA", 0.73292771, 1.17285559] + }, + { + "type": "double", + "attributes": {}, + "value": [0.6541665, 0.87389629, "NA", 0.98704296, 0.89822526] + }, + { + "type": "double", + "attributes": {}, + "value": [0.68478309, 1.19514308, "NA", 1.19565123, 1.12823353] + } + ] + } + +# draw_R seed + + { + "type": "double", + "attributes": {}, + "value": [0.94404007, 1.12731997, "NA", 0.9538139, 1.32909981] + } + +# estimate_advantage produces expected results (2 variants 3 locations) + + { + "type": "list", + "attributes": { + "names": { + "type": "character", + "attributes": {}, + "value": ["epsilon", "R", "convergence", "diag"] + } + }, + "value": [ + { + "type": "double", + "attributes": {}, + "value": [1.04125274, 1.04420411, 0.98760227, 0.9614608, 0.98635696] + }, + { + "type": "double", + "attributes": {}, + "value": [1.29132805, 0.99766874, 0.92202266, 0.97147169, 1.24692569] + }, + { + "type": "logical", + "attributes": {}, + "value": [true] + }, + { + "type": "list", + "attributes": {}, + "value": [ + { + "type": "list", + "attributes": { + "names": { + "type": "character", + "attributes": {}, + "value": ["psrf", "mpsrf"] + } + }, + "value": [ + { + "type": "double", + "attributes": { + "dim": { + "type": "integer", + "attributes": {}, + "value": [1, 2] + }, + "dimnames": { + "type": "list", + "attributes": {}, + "value": [ + { + "type": "NULL" + }, + { + "type": "character", + "attributes": {}, + "value": ["Point est.", "Upper C.I."] + } + ] + } + }, + "value": [0.99906146, 1.0075281] + }, + { + "type": "NULL" + } + ] + } + ] + } + ] + } + +# estimate_advantage produces expected results (2 variants 1 location) + + { + "type": "list", + "attributes": { + "names": { + "type": "character", + "attributes": {}, + "value": ["epsilon", "R", "convergence", "diag"] + } + }, + "value": [ + { + "type": "double", + "attributes": {}, + "value": [1.04271486, 0.95213998, 0.9775455, 1.00751841, 1.06043541] + }, + { + "type": "double", + "attributes": {}, + "value": [0.85713661, 0.99504745, 1.59755113, 0.94067038, 1.5918051] + }, + { + "type": "logical", + "attributes": {}, + "value": [true] + }, + { + "type": "list", + "attributes": {}, + "value": [ + { + "type": "list", + "attributes": { + "names": { + "type": "character", + "attributes": {}, + "value": ["psrf", "mpsrf"] + } + }, + "value": [ + { + "type": "double", + "attributes": { + "dim": { + "type": "integer", + "attributes": {}, + "value": [1, 2] + }, + "dimnames": { + "type": "list", + "attributes": {}, + "value": [ + { + "type": "NULL" + }, + { + "type": "character", + "attributes": {}, + "value": ["Point est.", "Upper C.I."] + } + ] + } + }, + "value": [0.99676223, 1.04637177] + }, + { + "type": "NULL" + } + ] + } + ] + } + ] + } + +# estimate_advantage produces expected results (>2 variants 4 locs) + + { + "type": "list", + "attributes": { + "names": { + "type": "character", + "attributes": {}, + "value": ["epsilon", "R", "convergence", "diag"] + } + }, + "value": [ + { + "type": "double", + "attributes": {}, + "value": [0.97699048, 0.97927712, 0.96709735, 1.0127237, 0.9741251] + }, + { + "type": "double", + "attributes": {}, + "value": [1.26994619, 1.11496927, 1.22484032, 0.78260032, 1.02935147] + }, + { + "type": "logical", + "attributes": {}, + "value": [true, true] + }, + { + "type": "list", + "attributes": {}, + "value": [ + { + "type": "list", + "attributes": { + "names": { + "type": "character", + "attributes": {}, + "value": ["psrf", "mpsrf"] + } + }, + "value": [ + { + "type": "double", + "attributes": { + "dim": { + "type": "integer", + "attributes": {}, + "value": [1, 2] + }, + "dimnames": { + "type": "list", + "attributes": {}, + "value": [ + { + "type": "NULL" + }, + { + "type": "character", + "attributes": {}, + "value": ["Point est.", "Upper C.I."] + } + ] + } + }, + "value": [0.98478887, 0.99198357] + }, + { + "type": "NULL" + } + ] + }, + { + "type": "list", + "attributes": { + "names": { + "type": "character", + "attributes": {}, + "value": ["psrf", "mpsrf"] + } + }, + "value": [ + { + "type": "double", + "attributes": { + "dim": { + "type": "integer", + "attributes": {}, + "value": [1, 2] + }, + "dimnames": { + "type": "list", + "attributes": {}, + "value": [ + { + "type": "NULL" + }, + { + "type": "character", + "attributes": {}, + "value": ["Point est.", "Upper C.I."] + } + ] + } + }, + "value": [0.99367021, 1.00567634] + }, + { + "type": "NULL" + } + ] + } + ] + } + ] + } + +# estimate_advantage produces expected results (>2 variants 1 loc) + + { + "type": "list", + "attributes": { + "names": { + "type": "character", + "attributes": {}, + "value": ["epsilon", "R", "convergence", "diag"] + } + }, + "value": [ + { + "type": "double", + "attributes": {}, + "value": [1.00070896, 1.01226561, 1.00211123, 1.03963539, 1.04309282] + }, + { + "type": "double", + "attributes": {}, + "value": [0.88871014, 0.88146824, 1.1264103, 0.96660343, 0.77642176] + }, + { + "type": "logical", + "attributes": {}, + "value": [true, false] + }, + { + "type": "list", + "attributes": {}, + "value": [ + { + "type": "list", + "attributes": { + "names": { + "type": "character", + "attributes": {}, + "value": ["psrf", "mpsrf"] + } + }, + "value": [ + { + "type": "double", + "attributes": { + "dim": { + "type": "integer", + "attributes": {}, + "value": [1, 2] + }, + "dimnames": { + "type": "list", + "attributes": {}, + "value": [ + { + "type": "NULL" + }, + { + "type": "character", + "attributes": {}, + "value": ["Point est.", "Upper C.I."] + } + ] + } + }, + "value": [1.06626227, 1.09796743] + }, + { + "type": "NULL" + } + ] + }, + { + "type": "list", + "attributes": { + "names": { + "type": "character", + "attributes": {}, + "value": ["psrf", "mpsrf"] + } + }, + "value": [ + { + "type": "double", + "attributes": { + "dim": { + "type": "integer", + "attributes": {}, + "value": [1, 2] + }, + "dimnames": { + "type": "list", + "attributes": {}, + "value": [ + { + "type": "NULL" + }, + { + "type": "character", + "attributes": {}, + "value": ["Point est.", "Upper C.I."] + } + ] + } + }, + "value": [1.08588305, 1.2273234] + }, + { + "type": "NULL" + } + ] + } + ] + } + ] + } + +# process_I_multivariant works as expected + + { + "type": "list", + "attributes": { + "names": { + "type": "character", + "attributes": {}, + "value": ["local", "imported"] + } + }, + "value": [ + { + "type": "double", + "attributes": {}, + "value": [10, 10, 10, 10, 10] + }, + { + "type": "double", + "attributes": {}, + "value": [0, 0, 0, 0, 0] + } + ] + } + +# estimate_advantage produces expected results (>2var, 1loc, imports) + + { + "type": "list", + "attributes": { + "names": { + "type": "character", + "attributes": {}, + "value": ["epsilon", "R", "convergence", "diag"] + } + }, + "value": [ + { + "type": "double", + "attributes": {}, + "value": [0.00042594, 0.00086427, 0.0002672, 0.00318991, 0.00002992] + }, + { + "type": "double", + "attributes": {}, + "value": [0.48364105, 0.93722817, 0.70361866, 0.84555007, 0.38715794] + }, + { + "type": "logical", + "attributes": {}, + "value": [true, false] + }, + { + "type": "list", + "attributes": {}, + "value": [ + { + "type": "list", + "attributes": { + "names": { + "type": "character", + "attributes": {}, + "value": ["psrf", "mpsrf"] + } + }, + "value": [ + { + "type": "double", + "attributes": { + "dim": { + "type": "integer", + "attributes": {}, + "value": [1, 2] + }, + "dimnames": { + "type": "list", + "attributes": {}, + "value": [ + { + "type": "NULL" + }, + { + "type": "character", + "attributes": {}, + "value": ["Point est.", "Upper C.I."] + } + ] + } + }, + "value": [1.02180146, 1.04177601] + }, + { + "type": "NULL" + } + ] + }, + { + "type": "list", + "attributes": { + "names": { + "type": "character", + "attributes": {}, + "value": ["psrf", "mpsrf"] + } + }, + "value": [ + { + "type": "double", + "attributes": { + "dim": { + "type": "integer", + "attributes": {}, + "value": [1, 2] + }, + "dimnames": { + "type": "list", + "attributes": {}, + "value": [ + { + "type": "NULL" + }, + { + "type": "character", + "attributes": {}, + "value": ["Point est.", "Upper C.I."] + } + ] + } + }, + "value": [1.30138256, 2.76090238] + }, + { + "type": "NULL" + } + ] + } + ] + } + ] + } + +# estimate_advantage produces expected results (>2var, 4loc, imports) + + { + "type": "list", + "attributes": { + "names": { + "type": "character", + "attributes": {}, + "value": ["epsilon", "R", "convergence", "diag"] + } + }, + "value": [ + { + "type": "double", + "attributes": {}, + "value": [6.8e-06, 0.00010827, 0.00009129, 0.0000513, 0.00009835] + }, + { + "type": "double", + "attributes": {}, + "value": [1.11202284, 1.62377233, 0.97466746, 1.4155541, 0.97185051] + }, + { + "type": "logical", + "attributes": {}, + "value": [false, true] + }, + { + "type": "list", + "attributes": {}, + "value": [ + { + "type": "list", + "attributes": { + "names": { + "type": "character", + "attributes": {}, + "value": ["psrf", "mpsrf"] + } + }, + "value": [ + { + "type": "double", + "attributes": { + "dim": { + "type": "integer", + "attributes": {}, + "value": [1, 2] + }, + "dimnames": { + "type": "list", + "attributes": {}, + "value": [ + { + "type": "NULL" + }, + { + "type": "character", + "attributes": {}, + "value": ["Point est.", "Upper C.I."] + } + ] + } + }, + "value": [1.08057539, 1.25813639] + }, + { + "type": "NULL" + } + ] + }, + { + "type": "list", + "attributes": { + "names": { + "type": "character", + "attributes": {}, + "value": ["psrf", "mpsrf"] + } + }, + "value": [ + { + "type": "double", + "attributes": { + "dim": { + "type": "integer", + "attributes": {}, + "value": [1, 2] + }, + "dimnames": { + "type": "list", + "attributes": {}, + "value": [ + { + "type": "NULL" + }, + { + "type": "character", + "attributes": {}, + "value": ["Point est.", "Upper C.I."] + } + ] + } + }, + "value": [0.99512604, 1.00472554] + }, + { + "type": "NULL" + } + ] + } + ] + } + ] + } + +# estimate_advantage produces expected results (2var, 1loc, imports) + + { + "type": "list", + "attributes": { + "names": { + "type": "character", + "attributes": {}, + "value": ["epsilon", "R", "convergence", "diag"] + } + }, + "value": [ + { + "type": "double", + "attributes": {}, + "value": [0.00158209, 0.00021575, 0.0001383, 0.00009621, 0.00039953] + }, + { + "type": "double", + "attributes": {}, + "value": [1.31434356, 0.6535476, 0.57880618, 1.11860124, 1.20305732] + }, + { + "type": "logical", + "attributes": {}, + "value": [true] + }, + { + "type": "list", + "attributes": {}, + "value": [ + { + "type": "list", + "attributes": { + "names": { + "type": "character", + "attributes": {}, + "value": ["psrf", "mpsrf"] + } + }, + "value": [ + { + "type": "double", + "attributes": { + "dim": { + "type": "integer", + "attributes": {}, + "value": [1, 2] + }, + "dimnames": { + "type": "list", + "attributes": {}, + "value": [ + { + "type": "NULL" + }, + { + "type": "character", + "attributes": {}, + "value": ["Point est.", "Upper C.I."] + } + ] + } + }, + "value": [0.99088731, 0.99936699] + }, + { + "type": "NULL" + } + ] + } + ] + } + ] + } + +# estimate_advantage produces expected results (2var, 4loc, imports) + + { + "type": "list", + "attributes": { + "names": { + "type": "character", + "attributes": {}, + "value": ["epsilon", "R", "convergence", "diag"] + } + }, + "value": [ + { + "type": "double", + "attributes": {}, + "value": [0.0003932, 0.00038339, 0.00005313, 0.00002397, 0.00025155] + }, + { + "type": "double", + "attributes": {}, + "value": [1.02125246, 1.53469409, 1.14810298, 1.46667941, 0.92564963] + }, + { + "type": "logical", + "attributes": {}, + "value": [true] + }, + { + "type": "list", + "attributes": {}, + "value": [ + { + "type": "list", + "attributes": { + "names": { + "type": "character", + "attributes": {}, + "value": ["psrf", "mpsrf"] + } + }, + "value": [ + { + "type": "double", + "attributes": { + "dim": { + "type": "integer", + "attributes": {}, + "value": [1, 2] + }, + "dimnames": { + "type": "list", + "attributes": {}, + "value": [ + { + "type": "NULL" + }, + { + "type": "character", + "attributes": {}, + "value": ["Point est.", "Upper C.I."] + } + ] + } + }, + "value": [1.03628412, 1.03693989] + }, + { + "type": "NULL" + } + ] + } + ] + } + ] + } + # estimate_advantage produces expected results (2 var, 2 loc, R_loc1 = 1.1, R_loc2 = 1.5) { @@ -74,3 +20951,195 @@ ] } +# estimate_advantage uses the correct t_min + + { + "type": "list", + "attributes": { + "names": { + "type": "character", + "attributes": {}, + "value": ["epsilon", "R", "convergence", "diag"] + } + }, + "value": [ + { + "type": "double", + "attributes": {}, + "value": [1.00471817, 1.03107137, 1.02035086, 1.00506093, 1.00732389] + }, + { + "type": "double", + "attributes": {}, + "value": [0.91800364, 0.85016542, 0.71860597, 1.07449868, 1.30864555] + }, + { + "type": "logical", + "attributes": {}, + "value": [false] + }, + { + "type": "list", + "attributes": {}, + "value": [ + { + "type": "list", + "attributes": { + "names": { + "type": "character", + "attributes": {}, + "value": ["psrf", "mpsrf"] + } + }, + "value": [ + { + "type": "double", + "attributes": { + "dim": { + "type": "integer", + "attributes": {}, + "value": [1, 2] + }, + "dimnames": { + "type": "list", + "attributes": {}, + "value": [ + { + "type": "NULL" + }, + { + "type": "character", + "attributes": {}, + "value": ["Point est.", "Upper C.I."] + } + ] + } + }, + "value": [1.02097152, 1.11993594] + }, + { + "type": "NULL" + } + ] + } + ] + } + ] + } + +# estimate_advantage convergence checks work with >2 variants + + { + "type": "list", + "attributes": { + "names": { + "type": "character", + "attributes": {}, + "value": ["epsilon", "R", "convergence", "diag"] + } + }, + "value": [ + { + "type": "double", + "attributes": {}, + "value": [0.99403141, 1.0081723, 1.03317077, 1.00100681, 0.97877066] + }, + { + "type": "double", + "attributes": {}, + "value": ["NA", 0.95176321, 1.13296226, 1.02441156, 1.44535094] + }, + { + "type": "logical", + "attributes": {}, + "value": [false, true] + }, + { + "type": "list", + "attributes": {}, + "value": [ + { + "type": "list", + "attributes": { + "names": { + "type": "character", + "attributes": {}, + "value": ["psrf", "mpsrf"] + } + }, + "value": [ + { + "type": "double", + "attributes": { + "dim": { + "type": "integer", + "attributes": {}, + "value": [1, 2] + }, + "dimnames": { + "type": "list", + "attributes": {}, + "value": [ + { + "type": "NULL" + }, + { + "type": "character", + "attributes": {}, + "value": ["Point est.", "Upper C.I."] + } + ] + } + }, + "value": [1.27871678, 1.98232008] + }, + { + "type": "NULL" + } + ] + }, + { + "type": "list", + "attributes": { + "names": { + "type": "character", + "attributes": {}, + "value": ["psrf", "mpsrf"] + } + }, + "value": [ + { + "type": "double", + "attributes": { + "dim": { + "type": "integer", + "attributes": {}, + "value": [1, 2] + }, + "dimnames": { + "type": "list", + "attributes": {}, + "value": [ + { + "type": "NULL" + }, + { + "type": "character", + "attributes": {}, + "value": ["Point est.", "Upper C.I."] + } + ] + } + }, + "value": [1.04497671, 1.07304128] + }, + { + "type": "NULL" + } + ] + } + ] + } + ] + } + From 3ab6a7dc9a278d7eccdd6d2afae6442372bd6a92 Mon Sep 17 00:00:00 2001 From: Steffi LaZerte Date: Wed, 3 Jun 2026 14:33:39 -0500 Subject: [PATCH 26/31] Fix doppleganger test paths --- R/test_utils.R | 4 ++-- tests/testthat/test-plot.R | 5 +++++ 2 files changed, 7 insertions(+), 2 deletions(-) diff --git a/R/test_utils.R b/R/test_utils.R index c3d42cac..dd7a605b 100644 --- a/R/test_utils.R +++ b/R/test_utils.R @@ -10,7 +10,7 @@ #' #' @noRd -epi_expect_doppelganger <- function(name, code, variant) { +epi_expect_doppelganger <- function(name, test, code, variant) { testthat::skip_if_not_installed("vdiffr") @@ -18,7 +18,7 @@ epi_expect_doppelganger <- function(name, code, variant) { no_snap <- !file.exists( testthat::test_path( "_snaps", - variant, + variant, tests, paste0(name, ".svg") ) ) diff --git a/tests/testthat/test-plot.R b/tests/testthat/test-plot.R index 8318b217..75ec8895 100644 --- a/tests/testthat/test-plot.R +++ b/tests/testthat/test-plot.R @@ -26,6 +26,7 @@ system <- Sys.info()[["sysname"]] test_that("plot.estimate_R doesn't have to include the legend", { epi_expect_doppelganger( "Flu2009-instantaneous-no-legend", + test = "plot", plot(R_i, legend = FALSE), variant = system ) |> suppressWarnings() # TODO: Fix use of aes_string in incidence to avoid this warning @@ -34,6 +35,7 @@ test_that("plot.estimate_R doesn't have to include the legend", { test_that("incidence can be plotted separately with imported cases", { epi_expect_doppelganger( "Flu2009-incidence-import", + test = "plot", plot(R_i, "incid", add_imported_cases=TRUE), variant = system ) |> suppressMessages() @@ -42,6 +44,7 @@ test_that("incidence can be plotted separately with imported cases", { test_that("serial interval distribution can be plotted separately", { epi_expect_doppelganger( "Flu2009-SI", + test = "plot", plot(R_i, "SI"), variant = system ) @@ -50,11 +53,13 @@ test_that("serial interval distribution can be plotted separately", { test_that("Reproduction numbers can be plotted separately", { epi_expect_doppelganger( "Flu2009-Ri", + test = "plot", plot(R_i, "R", options_R = list(ylim = c(0, 4))), variant = system ) epi_expect_doppelganger( "Flu2009-Rc", + test = "plot", plot(R_c, "R", options_R = list(ylim = c(0, 4))), variant = system ) From 92a87fb903df791a548ca8aee4002bb5f5a89611 Mon Sep 17 00:00:00 2001 From: Steffi LaZerte Date: Wed, 3 Jun 2026 14:33:55 -0500 Subject: [PATCH 27/31] Skip fragile tests where necessary --- tests/testthat/test-samplers.R | 2 ++ tests/testthat/test-wallinga_teunis.R | 9 +++++++++ 2 files changed, 11 insertions(+) diff --git a/tests/testthat/test-samplers.R b/tests/testthat/test-samplers.R index 1501921d..38f5c291 100644 --- a/tests/testthat/test-samplers.R +++ b/tests/testthat/test-samplers.R @@ -712,6 +712,8 @@ test_that("estimate_advantage produces expected results (2 var, 2 loc, R_loc1 = expect_equal(mean(x$R[,2,], na.rm = TRUE), 1.5, tolerance = 0.5) expect_s3_class(x$diag[[1]], "gelman.diag") + + skip_on_os("mac") # Consistently different from the other OS in far decimals epi_snapshot_value(x) }) diff --git a/tests/testthat/test-wallinga_teunis.R b/tests/testthat/test-wallinga_teunis.R index a332bc13..6cdc8889 100644 --- a/tests/testthat/test-wallinga_teunis.R +++ b/tests/testthat/test-wallinga_teunis.R @@ -271,6 +271,14 @@ test_that( test_that("Example 1 matches saved output", { skip_on_os(os = "mac") # Consistently different from the other OS + + skip_if(grepl("unstable", R.version$status)) # Skip on Devel + # NOTE: Expect this to fail on R-devel and therefore on next R release + # likely because of a change in rmultinom(), not a bug, but a difference in + # randomization + # https://bugs.r-project.org/show_bug.cgi?id=18693&_ts=1780518376 + # + # TODO: Update the snapshots on next release and skip on previous releases data("Flu2009") set.seed(1) @@ -278,6 +286,7 @@ test_that("Example 1 matches saved output", { Flu2009$incidence, method = "non_parametric_si", config = list( + seed = 1, t_start = 2:26, t_end = 8:32, si_distr = Flu2009$si_distr, From f85463ef078a97f2b197d2397cb85f8c151a51b3 Mon Sep 17 00:00:00 2001 From: Steffi LaZerte Date: Wed, 3 Jun 2026 14:44:09 -0500 Subject: [PATCH 28/31] Correct test path in doppleganger --- R/test_utils.R | 5 +---- 1 file changed, 1 insertion(+), 4 deletions(-) diff --git a/R/test_utils.R b/R/test_utils.R index dd7a605b..dbd23f9f 100644 --- a/R/test_utils.R +++ b/R/test_utils.R @@ -16,10 +16,7 @@ epi_expect_doppelganger <- function(name, test, code, variant) { on_ci <- as.logical(Sys.getenv("CI", "false")) no_snap <- !file.exists( - testthat::test_path( - "_snaps", - variant, tests, - paste0(name, ".svg") + testthat::test_path("_snaps", variant, test, paste0(name, ".svg") ) ) if (on_ci && no_snap) { From 0593b22bd3e074e761de41e1ff2d6dc2a65e63e0 Mon Sep 17 00:00:00 2001 From: Steffi LaZerte Date: Fri, 5 Jun 2026 15:56:43 -0500 Subject: [PATCH 29/31] Add tests for plots of wallinga_teunis results (fixes #204) --- .../plot/flu2009-rc-incidence-import.svg | 145 ++++++++++ .../flu2009-rc-instantaneous-no-legend.svg | 257 ++++++++++++++++++ .../_snaps/Linux/plot/flu2009-rc-si.svg | 71 +++++ tests/testthat/test-plot.R | 22 +- 4 files changed, 494 insertions(+), 1 deletion(-) create mode 100644 tests/testthat/_snaps/Linux/plot/flu2009-rc-incidence-import.svg create mode 100644 tests/testthat/_snaps/Linux/plot/flu2009-rc-instantaneous-no-legend.svg create mode 100644 tests/testthat/_snaps/Linux/plot/flu2009-rc-si.svg diff --git a/tests/testthat/_snaps/Linux/plot/flu2009-rc-incidence-import.svg b/tests/testthat/_snaps/Linux/plot/flu2009-rc-incidence-import.svg new file mode 100644 index 00000000..3d0c05dd --- /dev/null +++ b/tests/testthat/_snaps/Linux/plot/flu2009-rc-incidence-import.svg @@ -0,0 +1,145 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +0.0 +2.5 +5.0 +7.5 +10.0 +12.5 + + + + + + + + + + + + +2009-04-27 +2009-05-04 +2009-05-11 +2009-05-18 +2009-05-25 +Time +Incidence +Epidemic curve + + diff --git a/tests/testthat/_snaps/Linux/plot/flu2009-rc-instantaneous-no-legend.svg b/tests/testthat/_snaps/Linux/plot/flu2009-rc-instantaneous-no-legend.svg new file mode 100644 index 00000000..9fdbe81c --- /dev/null +++ b/tests/testthat/_snaps/Linux/plot/flu2009-rc-instantaneous-no-legend.svg @@ -0,0 +1,257 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +0.0 +2.5 +5.0 +7.5 +10.0 +12.5 + + + + + + + + + + + + +2009-04-27 +2009-05-04 +2009-05-11 +2009-05-18 +2009-05-25 +Time +Incidence +Epidemic curve + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +0.0 +0.5 +1.0 +1.5 + + + + + + + + + + +Apr 27 +May 04 +May 11 +May 18 +May 25 +Time +R +Estimated R + + + + + + + + + + + + + + + + + + + + + + + + + + + + +0.0 +0.1 +0.2 +0.3 + + + + + + + + + +0 +3 +6 +9 +Time +Frequency +Explored SI distribution +Flu2009-Rc-instantaneous-no-legend + + diff --git a/tests/testthat/_snaps/Linux/plot/flu2009-rc-si.svg b/tests/testthat/_snaps/Linux/plot/flu2009-rc-si.svg new file mode 100644 index 00000000..a2b43089 --- /dev/null +++ b/tests/testthat/_snaps/Linux/plot/flu2009-rc-si.svg @@ -0,0 +1,71 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +0.0 +0.1 +0.2 +0.3 + + + + + + + + + +0 +3 +6 +9 +Time +Frequency +Explored SI distribution + + diff --git a/tests/testthat/test-plot.R b/tests/testthat/test-plot.R index 75ec8895..8355aa1c 100644 --- a/tests/testthat/test-plot.R +++ b/tests/testthat/test-plot.R @@ -30,13 +30,27 @@ test_that("plot.estimate_R doesn't have to include the legend", { plot(R_i, legend = FALSE), variant = system ) |> suppressWarnings() # TODO: Fix use of aes_string in incidence to avoid this warning + + epi_expect_doppelganger( + "Flu2009-Rc-instantaneous-no-legend", + test = "plot", + plot(R_c, legend = FALSE), + variant = system + ) |> suppressWarnings() # TODO: Fix use of aes_string in incidence to avoid this warning }) test_that("incidence can be plotted separately with imported cases", { epi_expect_doppelganger( "Flu2009-incidence-import", test = "plot", - plot(R_i, "incid", add_imported_cases=TRUE), + plot(R_i, "incid", add_imported_cases = TRUE), + variant = system + ) |> suppressMessages() + + epi_expect_doppelganger( + "Flu2009-Rc-incidence-import", + test = "plot", + plot(R_c, "incid", add_imported_cases = TRUE), variant = system ) |> suppressMessages() }) @@ -48,6 +62,12 @@ test_that("serial interval distribution can be plotted separately", { plot(R_i, "SI"), variant = system ) + epi_expect_doppelganger( + "Flu2009-Rc-SI", + test = "plot", + plot(R_c, "SI"), + variant = system + ) }) test_that("Reproduction numbers can be plotted separately", { From 07444a526051e79f76a8a51dd748187b3831af25 Mon Sep 17 00:00:00 2001 From: Steffi LaZerte Date: Wed, 17 Jun 2026 10:32:39 -0500 Subject: [PATCH 30/31] Move incidence and incidence2 to suggests (fixes #241) --- DESCRIPTION | 4 ++-- NEWS.md | 6 ++++++ R/estimate_R.R | 7 +++++++ R/plot.R | 1 + R/wallinga_teunis.R | 4 ++++ 5 files changed, 20 insertions(+), 2 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index ff9754a9..75992cc7 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -30,8 +30,6 @@ BugReports: https://github.com/mrc-ide/EpiEstim/issues Depends: R (>= 3.3.0) Imports: coarseDataTools (>= 0.6-4), - incidence (>= 1.7.0), - incidence2 (>= 2.6.4.9000), ggplot2, fitdistrplus, coda, @@ -42,6 +40,8 @@ Imports: patchwork (>= 1.2.0), rlang Suggests: + incidence (>= 1.7.0), + incidence2 (>= 2.6.4.9000), testthat (>= 3.0.0), utils, vdiffr, diff --git a/NEWS.md b/NEWS.md index 9b6c5a15..8e42bbf8 100644 --- a/NEWS.md +++ b/NEWS.md @@ -1,3 +1,9 @@ +# EpiEstim 3.0.1 + +- Moved incidence and incidence2 to suggests `walling_teunis()` and + `estimate_R()` will check for installations as required (fixes + [#241](https://github.com/mrc-ide/EpiEstim/issues/241)) + # EpiEstim 3.0.0 ## New features diff --git a/R/estimate_R.R b/R/estimate_R.R index 8eef67af..7a19984c 100755 --- a/R/estimate_R.R +++ b/R/estimate_R.R @@ -361,6 +361,13 @@ estimate_R <- function(incid, date_convention = NULL ) { + if(inherits(incid, "incidence")) { + rlang::check_installed("incidence", "to work with incidence data") + } + if(inherits(incid, "incidence2")) { + rlang::check_installed("incidence2", "to work with incidence2 data") + } + if (is.data.frame(incid) && "dates" %in% names(incid)) { if (!all(diff(incid$dates) > 0)) { stop("dates in incid must be in ascending order", call. = FALSE) diff --git a/R/plot.R b/R/plot.R index 3658686b..23c1eb27 100644 --- a/R/plot.R +++ b/R/plot.R @@ -264,6 +264,7 @@ plot.estimate_R <- function(x, what = c("all", "incid", "R", "SI"), plot_theme = what <- match.arg(what) if (what %in% c("incid", "all")) { + rlang::check_installed("incidence", "to plot incidence data") if (add_imported_cases) { p1 <- plot( incidence::as.incidence(incid, diff --git a/R/wallinga_teunis.R b/R/wallinga_teunis.R index 7a28dcbc..8febaf05 100644 --- a/R/wallinga_teunis.R +++ b/R/wallinga_teunis.R @@ -416,6 +416,8 @@ wallinga_teunis.incidence <- function(incid, quiet = FALSE, ...) { + rlang::check_installed("incidence", "to work with incidence data") + ## checks specific to incidence objects if (as.integer(mean(incidence::get_interval(incid))) != 1L) { msg <- sprintf( @@ -457,6 +459,8 @@ wallinga_teunis.incidence2 <- function(incid, quiet = FALSE, ...) { + rlang::check_installed("incidence2", "to work with incidence2 data") + ## checks specific to incidence2 objects dates <- incidence2::get_dates(incid) interval <- incidence2::get_interval_duration(incid) From 479c7f2892e7e076ab2f3066d25f0acf511b2926 Mon Sep 17 00:00:00 2001 From: Steffi LaZerte Date: Wed, 17 Jun 2026 10:52:12 -0500 Subject: [PATCH 31/31] Add mocked tests to for installation check --- tests/testthat/test-estimate_R.R | 36 +++++++++++++++++++ tests/testthat/test-wallinga_teunis.R | 51 +++++++++++++++++++++++++++ 2 files changed, 87 insertions(+) diff --git a/tests/testthat/test-estimate_R.R b/tests/testthat/test-estimate_R.R index 2a0f7c85..2d420920 100644 --- a/tests/testthat/test-estimate_R.R +++ b/tests/testthat/test-estimate_R.R @@ -9,6 +9,42 @@ test_that("There is an informative error if dates are not ascending", { "dates in incid must be in ascending order") }) +test_that("estimate_R() checks installed", { + local_mocked_bindings( + check_installed = function(pkg, reason) { + stop(pkg, " needed ", reason) + }, .package = "rlang") + + + # No incidence data + expect_silent( + estimate_R( + Flu2009$incidence, + config = list( + t_start = 2:26, + t_end = 8:32, + si_distr = Flu2009$si_distr, + seed = 1 + ) + ) + ) + + # incidence Data + data <- c(0, 1, 1, 2, 1, 3, 4, 5, 5, 5, 5, 4, 4, 26, 6, 7, 9) + location <- c("imported", "local", "imported", "imported", "local", + "imported", "imported", "imported", "imported", + "local", "local", "local", "imported", "local", + "imported", "local", "imported") + incid <- incidence::incidence(data, groups = location) + expect_error(estimate_R(incid), "incidence needed to work with incidence data") + + # incidence2 Data + i <- rpois(100, lambda = exp(0.0523 * 1:100)) + onset <- rep(Sys.Date() + 1:100, i) + incid <- incidence2::incidence(data.frame(onset), "onset") + expect_error(estimate_R(incid), "incidence2 needed to work with incidence2 data") +}) + # The following examples are from EpiEstim's documentation. diff --git a/tests/testthat/test-wallinga_teunis.R b/tests/testthat/test-wallinga_teunis.R index 6cdc8889..7c907216 100644 --- a/tests/testthat/test-wallinga_teunis.R +++ b/tests/testthat/test-wallinga_teunis.R @@ -147,6 +147,23 @@ test_that("wallinga_teunis() works with data.frame inputs", { expect_equal(res_df$dates, df$dates) expect_equal(res_df$I, res_i$I) + # checks installed + local_mocked_bindings( + check_installed = function(pkg, reason) { + stop(pkg, " needed ", reason) + }, .package = "rlang") + + expect_silent( + wallinga_teunis( + df, + method = "non_parametric_si", + config = list(t_start = 10, + t_end = 90, + si_distr = Flu2009$si_distr, + seed = 1, + n_sim = 50), + count = "incid" + )) }) @@ -180,6 +197,23 @@ test_that("wallinga_teunis() works with incidence inputs", { expect_equal(res_df$dates, df$dates) expect_equal(res_incid$I, res_df$I) + # checks installed + local_mocked_bindings( + check_installed = function(pkg, reason) { + stop(pkg, " needed ", reason) + }, .package = "rlang") + + expect_error( + wallinga_teunis( + incid, + method = "non_parametric_si", + config = list(t_start = 10, t_end = 90, + si_distr = Flu2009$si_distr, + seed = 1, + n_sim = 50) + ), "incidence needed to work with incidence data" + ) + }) @@ -215,6 +249,23 @@ test_that("wallinga_teunis() works with incidence2 inputs", { expect_equal(res_incid$dates, res_df$dates) expect_equal(res_incid$I, res_df$I) + # checks installed + local_mocked_bindings( + check_installed = function(pkg, reason) { + stop(pkg, " needed ", reason) + }, .package = "rlang") + + expect_error( + wallinga_teunis( + incid, + method = "non_parametric_si", + config = list(t_start = 10, t_end = 90, + si_distr = Flu2009$si_distr, + seed = 1, + n_sim = 50) + ), + "incidence2 needed to work with incidence2 data" + ) })