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Copy path125_function_modelcode.R
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377 lines (343 loc) · 12 KB
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get_jags_model_code <- function(bs = "tp",
k_code = 5,
type = "leftcensored"){
#
# Censored without measurement error ----
#
if (bs == "tp" & k_code == 1 & type == "leftcensored"){
#
code <- '
model {
b_new <- c(b[1], b2, 0) # Added line
mu <- X %*% b_new ## expected response
for (i in 1:n) { y[i] ~ dnorm(mu[i], tau) } ## response
for (j in 1:m) {
Z[j] ~ dbern(prob[j])
prob[j] <- min(max(pnorm(cut[j], mu[n+j], tau), 0.01),0.99)
}
scale <- 1/tau ## convert tau to standard GLM scale
tau ~ dgamma(.05,.005) ## precision parameter prior
## Parametric effect priors CHECK tau=1/79^2 is appropriate!
for (i in 1:1) { b[i] ~ dnorm(0,0.00016) }
## prior for s(x2)...
# K1 <- S1[1,1] * lambda[1] + S1[1,3] * lambda[2] # Changed from the code for k_code = 3
# b[2] ~ dmnorm(zero[3], K1) # Changed from the code for k_code = 3
## smoothing parameter priors CHECK...
for (i in 1:2) {
lambda[i] ~ dgamma(.05,.005)
rho[i] <- log(lambda[i])
}
}
'
} else if (bs == "tp" & k_code == 2 & type == "leftcensored"){
#
code <- '
model {
b_new <- c(b[1], b2, b[2]) # Added line
mu <- X %*% b_new ## expected response
# Difference mu - reference mu (observed only if reference_x is set):
for (t in 1:(t2-t1+1)) {
dmu[t] <- mu[t1+t-1]-mu[t_ref]
}
for (i in 1:n) { y[i] ~ dnorm(mu[i], tau) } ## response
for (j in 1:m) {
Z[j] ~ dbern(prob[j])
prob[j] <- min(max(pnorm(cut[j], mu[n+j], tau), 0.01),0.99)
}
scale <- 1/tau ## convert tau to standard GLM scale
tau ~ dgamma(.05,.005) ## precision parameter prior
## Parametric effect priors CHECK tau=1/79^2 is appropriate!
for (i in 1:1) { b[i] ~ dnorm(0,0.00016) }
## prior for s(x2)...
K1 <- S1[1,1] * lambda[1] + S1[1,3] * lambda[2] # Changed from the code for k_code = 3
b[2] ~ dmnorm(zero[3], K1) # Changed from the code for k_code = 3
## smoothing parameter priors CHECK...
for (i in 1:2) {
lambda[i] ~ dgamma(.05,.005)
rho[i] <- log(lambda[i])
}
}
'
} else if (bs == "tp" & k_code >= 3 & type == "leftcensored"){
code <- '
model {
mu <- X %*% b ## expected response
# Difference mu - reference mu (observed only if reference_x is set):
for (t in 1:(t2-t1+1)) {
dmu[t] <- mu[t1+t-1]-mu[t_ref]
}
for (i in 1:n) { y[i] ~ dnorm(mu[i], tau) } ## response
for (j in 1:m) {
Z[j] ~ dbern(prob[j])
prob[j] <- min(max(pnorm(cut[j], mu[n+j], tau), 0.01),0.99)
}
scale <- 1/tau ## convert tau to standard GLM scale
tau ~ dgamma(.05,.005) ## precision parameter prior
## Parametric effect priors CHECK tau=1/79^2 is appropriate!
for (i in 1:1) { b[i] ~ dnorm(0,0.00016) }
## prior for s(x2)...
for (i in c(2:(k-1))) { b[i] ~ dnorm(0, lambda[1]) }
for (i in c(k)) { b[i] ~ dnorm(0, lambda[2]) }
## smoothing parameter priors CHECK...
for (i in 1:2) {
lambda[i] ~ dgamma(.05,.005)
rho[i] <- log(lambda[i])
}
}
'
#
# Censored with measurement error ----
#
} else if (bs == "tp" & k_code == 1 & type == "leftcensored_measerror"){
code <- '
model {
b_new <- c(b[1], b2, 0) # Added line
mu <- X %*% b_new ## expected response
for (i in 1:n) {
y[i] ~ dnorm(mu[i], total_var[i]^-1) ## response
total_var[i] <- scale^2 + meas_error[i]^2
}
for (j in 1:m) {
Z[j] ~ dbern(prob[j])
prob[j] <- min(max(pnorm(cut[j], mu[n+j], tau), 0.01),0.99)
}
scale <- 1/tau ## convert tau to standard GLM scale
tau ~ dgamma(.05,.005) ## precision parameter prior
## Parametric effect priors CHECK tau=1/79^2 is appropriate!
for (i in 1:1) { b[i] ~ dnorm(0,0.00016) }
## prior for s(x2)...
# K1 <- S1[1,1] * lambda[1] + S1[1,3] * lambda[2] # Changed from the code for k_code = 3
# b[2] ~ dmnorm(zero[3], K1) # Changed from the code for k_code = 3
## smoothing parameter priors CHECK...
for (i in 1:2) {
lambda[i] ~ dgamma(.05,.005)
rho[i] <- log(lambda[i])
}
}
'
} else if (bs == "tp" & k_code == 2 & type == "leftcensored_measerror"){
code <- '
model {
b_new <- c(b[1], b2, b[2]) # Added line
mu <- X %*% b_new ## expected response
# Difference mu - reference mu (observed only if reference_x is set):
for (t in 1:(t2-t1+1)) {
dmu[t] <- mu[t1+t-1]-mu[t_ref]
}
for (i in 1:n) {
y[i] ~ dnorm(mu[i], total_var[i]^-1) ## response
total_var[i] <- scale^2 + meas_error[i]^2
}
for (j in 1:m) {
Z[j] ~ dbern(prob[j])
prob[j] <- min(max(pnorm(cut[j], mu[n+j], tau), 0.01),0.99)
}
scale <- 1/tau ## convert tau to standard GLM scale
tau ~ dgamma(.05,.005) ## precision parameter prior
## Parametric effect priors CHECK tau=1/79^2 is appropriate!
for (i in 1:1) { b[i] ~ dnorm(0,0.00016) }
## prior for s(x2)...
K1 <- S1[1,1] * lambda[1] + S1[1,3] * lambda[2] # Changed from the code for k_code = 3
b[2] ~ dmnorm(zero[3], K1) # Changed from the code for k_code = 3
## smoothing parameter priors CHECK...
for (i in 1:2) {
lambda[i] ~ dgamma(.05,.005)
rho[i] <- log(lambda[i])
}
}
'
} else if (bs == "tp" & k_code >= 3 & type == "leftcensored_measerror"){
code <- '
model {
mu <- X %*% b ## expected response
# Difference mu - reference mu (observed only if reference_x is set):
for (t in 1:(t2-t1+1)) {
dmu[t] <- mu[t1+t-1]-mu[t_ref]
}
for (i in 1:n) {
y[i] ~ dnorm(mu[i], total_var[i]^-1) ## response
total_var[i] <- scale^2 + meas_error[i]^2
}
for (j in 1:m) {
Z[j] ~ dbern(prob[j])
prob[j] <- min(max(pnorm(cut[j], mu[n+j], tau), 0.01),0.99)
}
scale <- 1/tau ## convert tau to standard GLM scale
tau ~ dgamma(.05,.005) ## precision parameter prior
## Parametric effect priors CHECK tau=1/79^2 is appropriate!
for (i in 1:1) { b[i] ~ dnorm(0,0.00016) }
## prior for s(x2)...
for (i in c(2:(k-1))) { b[i] ~ dnorm(0, lambda[1]) }
for (i in c(k)) { b[i] ~ dnorm(0, lambda[2]) }
## smoothing parameter priors CHECK...
for (i in 1:2) {
lambda[i] ~ dgamma(.05,.005)
rho[i] <- log(lambda[i])
}
}
'
#
# Uncensored with measurement error ----
#
# Code: as censored, just deleting the 4 lines:
# for (j in 1:m) {
# Z[j] ~ dbern(prob[j])
# prob[j] <- min(max(pnorm(cut[j], mu[n+j], tau), 0.01),0.99)
# }
#
# and not supplying "cut", "Z" and "m" in the data, see get_jagam_object
# for the case of m == 0
} else if (bs == "tp" & k_code == 1 & type == "uncensored"){
#
code <- '
model {
b_new <- c(b[1], b2, 0) # Added line
mu <- X %*% b_new ## expected response
for (i in 1:n) { y[i] ~ dnorm(mu[i], tau) } ## response
scale <- 1/tau ## convert tau to standard GLM scale
tau ~ dgamma(.05,.005) ## precision parameter prior
## Parametric effect priors CHECK tau=1/79^2 is appropriate!
for (i in 1:1) { b[i] ~ dnorm(0,0.00016) }
## prior for s(x2)...
# K1 <- S1[1,1] * lambda[1] + S1[1,3] * lambda[2] # Changed from the code for k_code = 3
# b[2] ~ dmnorm(zero[3], K1) # Changed from the code for k_code = 3
## smoothing parameter priors CHECK...
for (i in 1:2) {
lambda[i] ~ dgamma(.05,.005)
rho[i] <- log(lambda[i])
}
}
'
} else if (bs == "tp" & k_code == 2 & type == "uncensored"){
#
code <- '
model {
b_new <- c(b[1], b2, b[2]) # Added line
mu <- X %*% b_new ## expected response
# Difference mu - reference mu (observed only if reference_x is set):
for (t in 1:(t2-t1+1)) {
dmu[t] <- mu[t1+t-1]-mu[t_ref]
}
for (i in 1:n) { y[i] ~ dnorm(mu[i], tau) } ## response
scale <- 1/tau ## convert tau to standard GLM scale
tau ~ dgamma(.05,.005) ## precision parameter prior
## Parametric effect priors CHECK tau=1/79^2 is appropriate!
for (i in 1:1) { b[i] ~ dnorm(0,0.00016) }
## prior for s(x2)...
K1 <- S1[1,1] * lambda[1] + S1[1,3] * lambda[2] # Changed from the code for k_code = 3
b[2] ~ dmnorm(zero[3], K1) # Changed from the code for k_code = 3
## smoothing parameter priors CHECK...
for (i in 1:2) {
lambda[i] ~ dgamma(.05,.005)
rho[i] <- log(lambda[i])
}
}
'
} else if (bs == "tp" & k_code >= 3 & type == "uncensored"){
code <- '
model {
mu <- X %*% b ## expected response
# Difference mu - reference mu (observed only if reference_x is set):
for (t in 1:(t2-t1+1)) {
dmu[t] <- mu[t1+t-1]-mu[t_ref]
}
for (i in 1:n) { y[i] ~ dnorm(mu[i], tau) } ## response
scale <- 1/tau ## convert tau to standard GLM scale
tau ~ dgamma(.05,.005) ## precision parameter prior
## Parametric effect priors CHECK tau=1/79^2 is appropriate!
for (i in 1:1) { b[i] ~ dnorm(0,0.00016) }
## prior for s(x2)...
for (i in c(2:(k-1))) { b[i] ~ dnorm(0, lambda[1]) }
for (i in c(k)) { b[i] ~ dnorm(0, lambda[2]) }
## smoothing parameter priors CHECK...
for (i in 1:2) {
lambda[i] ~ dgamma(.05,.005)
rho[i] <- log(lambda[i])
}
}
'
#
# Uncensored with measurement error ----
#
} else if (bs == "tp" & k_code == 1 & type == "uncensored_measerror"){
code <- '
model {
b_new <- c(b[1], b2, 0) # Added line
mu <- X %*% b_new ## expected response
for (i in 1:n) {
y[i] ~ dnorm(mu[i], total_var[i]^-1) ## response
total_var[i] <- scale^2 + meas_error[i]^2
}
scale <- 1/tau ## convert tau to standard GLM scale
tau ~ dgamma(.05,.005) ## precision parameter prior
## Parametric effect priors CHECK tau=1/79^2 is appropriate!
for (i in 1:1) { b[i] ~ dnorm(0,0.00016) }
## prior for s(x2)...
# K1 <- S1[1,1] * lambda[1] + S1[1,3] * lambda[2] # Changed from the code for k_code = 3
# b[2] ~ dmnorm(zero[3], K1) # Changed from the code for k_code = 3
## smoothing parameter priors CHECK...
for (i in 1:2) {
lambda[i] ~ dgamma(.05,.005)
rho[i] <- log(lambda[i])
}
}
'
} else if (bs == "tp" & k_code == 2 & type == "uncensored_measerror"){
code <- '
model {
b_new <- c(b[1], b2, b[2]) # Added line
mu <- X %*% b_new ## expected response
# Difference mu - reference mu (observed only if reference_x is set):
for (t in 1:(t2-t1+1)) {
dmu[t] <- mu[t1+t-1]-mu[t_ref]
}
for (i in 1:n) {
y[i] ~ dnorm(mu[i], total_var[i]^-1) ## response
total_var[i] <- scale^2 + meas_error[i]^2
}
scale <- 1/tau ## convert tau to standard GLM scale
tau ~ dgamma(.05,.005) ## precision parameter prior
## Parametric effect priors CHECK tau=1/79^2 is appropriate!
for (i in 1:1) { b[i] ~ dnorm(0,0.00016) }
## prior for s(x2)...
K1 <- S1[1,1] * lambda[1] + S1[1,3] * lambda[2] # Changed from the code for k_code = 3
b[2] ~ dmnorm(zero[3], K1) # Changed from the code for k_code = 3
## smoothing parameter priors CHECK...
for (i in 1:2) {
lambda[i] ~ dgamma(.05,.005)
rho[i] <- log(lambda[i])
}
}
'
} else if (bs == "tp" & k_code >= 3 & type == "uncensored_measerror"){
code <- '
model {
mu <- X %*% b ## expected response
# Difference mu - reference mu (observed only if reference_x is set):
for (t in 1:(t2-t1+1)) {
dmu[t] <- mu[t1+t-1]-mu[t_ref]
}
for (i in 1:n) {
y[i] ~ dnorm(mu[i], total_var[i]^-1) ## response
total_var[i] <- scale^2 + meas_error[i]^2
}
scale <- 1/tau ## convert tau to standard GLM scale
tau ~ dgamma(.05,.005) ## precision parameter prior
## Parametric effect priors CHECK tau=1/79^2 is appropriate!
for (i in 1:1) { b[i] ~ dnorm(0,0.00016) }
## prior for s(x2)...
for (i in c(2:(k-1))) { b[i] ~ dnorm(0, lambda[1]) }
for (i in c(k)) { b[i] ~ dnorm(0, lambda[2]) }
## smoothing parameter priors CHECK...
for (i in 1:2) {
lambda[i] ~ dgamma(.05,.005)
rho[i] <- log(lambda[i])
}
}
'
} else {
stop("The given combination of bs = ", sQuote(bs), ", k_code = ", k_code, ", and type = ", sQuote(type),
" has not been implemented.")
}
code
}