1d:T78c,# K-fold cross-validation for Kernel Logistic Regression The function performs k-fold cross validation for kernel logistic regression to estimate tuning parameters. ```r 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. ## Returns - **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`. ## Examples ```r 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) ``` ## Author(s) Chih-Li Sung ## See Also `KLR` for performing a kernel logistic regression with given `lambda` and `rho`.

cv.KLR function