KLR

Kernel Logistic Regression

Installation

Install the devtools package.

Install this package using:

devtools::install_github("fontaine618/KLR")

Example

Generate data:

set.seed(1)
n = 200
p = 3
y = runif(n) > 0.2
y = y + 1
mu = matrix(rnorm(2*p), 2, p)
x = mu[y, ]
x = x + matrix(rnorm(n*p), n, p)

Fit the model and get contours:

library(KLR)
KLRobj = KLR(y, x, kernel="polynomial", lambda=0.001, sigma2=2.0, d=3)
object = KLR(y, x, "gaussian", lambda=1.0, sigma2=0.5)
contours = contours.KLR(object, dims=1:2, res=100, level=0.5)
plot(x[, 1], x[, 2], col=y)
sapply(contours, function(c) lines(c$x, c$y, type="l"))

Get predictions using the predict base function:

predict(KLRobj, newdata) # returns probabilities

Perform cross-validation for the tuning parameters:

cv.KLR(y, x, n_folds=10, lambda=c(0.001, 0.01, 0.1, 1), sigma2=c(0.1,0.5,1.0,2.0))