calibrateBinary (version 0.1)

cv.KLR: K-fold cross-validation for Kernel Logistic Regression

Description

The function performs k-fold cross validation for kernel logistic regression to estimate tuning parameters.

Usage

cv.KLR(X, y, K = 5, lambda = seq(0.001, 0.2, 0.005), kernel = c("matern",
  "exponential")[1], nu = 1.5, power = 1.95, rho = seq(0.05, 0.5, 0.05))

Arguments

X

input for KLR.

y

input for KLR.

K

a positive integer specifying the number of folds. The default is 5.

lambda

a vector specifying lambda values at which CV curve will be computed.

kernel

input for KLR.

nu

input for KLR.

power

input for KLR.

rho

rho value at which CV curve will be computed.

Value

lambda

value of lambda that gives minimum CV error.

rho

value of rho that gives minimum CV error.

Details

This function performs the k-fold cross-valibration for a kernel logistic regression. The CV curve is computed at the values of the tuning parameters assigned by lambda and rho. The number of fold is given by K.

See Also

KLR for performing a kernel logistic regression with given lambda and rho.

Examples

# NOT RUN {
library(calibrateBinary)

set.seed(1)
np <- 10
xp <- seq(0,1,length.out = np)
eta_fun <- function(x) exp(exp(-0.5*x)*cos(3.5*pi*x)-1) # true probability function
eta_x <- eta_fun(xp)
yp <- rep(0,np)
for(i in 1:np) yp[i] <- rbinom(1,1, eta_x[i])

x.test <- seq(0,1,0.001)
etahat <- KLR(xp,yp,x.test)

plot(xp,yp)
curve(eta_fun, col = "blue", lty = 2, add = TRUE)
lines(x.test, etahat, col = 2)

#####   cross-validation with K=5    #####
##### to determine the parameter rho #####

cv.out <- cv.KLR(xp,yp,K=5)
print(cv.out)

etahat.cv <- KLR(xp,yp,x.test,lambda=cv.out$lambda,rho=cv.out$rho)

plot(xp,yp)
curve(eta_fun, col = "blue", lty = 2, add = TRUE)
lines(x.test, etahat, col = 2)
lines(x.test, etahat.cv, col = 3)

# }