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##'Cross-Validation for ELMCoxBAR to tune kernel parameters Based on log Likelihood
##' @title Cross-Validation for ELMCoxBAR
##' @param x The covariates(predictor variables) of training data.
##' @param y Survival time and censored status of training data. Must be a Surv \code{survival} object
##' @param Kernel_type Type of kernel matrix. Currently four options avaibable. "RBF_kernel",a RBF kernel;"lin_kernel" , a linear kernel;poly_kernel ,a polynomial kernel;sigmoid_kernel, a sigmoid kernel. Default is "lin_kernel".
##' @param Kernel_para Parameters for different types of kernels. A single value for RBF and linear kernels. A vector for polynomial and sigmoid kernels and progam stops if only a single value is supplied. However, if the vector of values is supplied in the cases of RBF and liner kernels, only the first value will be used. Default is a vector value "c(2,1)".
##' @param penality Currently, penality is defaulted to 0 to train an ELMCoxBAR model.
##' @param maxiter Maximum values of iterations to update the CoxBAR estimator. Default is 5.
##' @param nfolds Number of folds in cross validation.
##' @param ... Additional arguments for glmnet.
##' @return Object of class \code{ELMCoxBAR} with elements
##' \tabular{ll}{
##' \code{elmcox} \tab A glmnet type model. See \code{glmnet} for details. \cr
##' \code{trainx} \tab Training data covariates. \cr
##' \code{kerneltype} \tab Type of kernel matrix used in training. kerneltype=1,a RBF kernel;kerneltype=2 , a linear kernel;kerneltype=3 ,a polynomial kernel;kerneltype=4, a sigmoid kernel. \cr
##' \code{Kernel_para} \tab Optimal Parameters returned by cross validation. A single value for kerneltype=1 or 2. A vector for kerneltype=3 or 4. \cr
##' }
##' @author Hong Wang
##' @references
##' \itemize{
##' \item Wang, H, Li, G. Extreme learning machine Cox model for high-dimensional survival analysis. Statistics in Medicine. 2019; 38:2139-2156.
##' }
##' @examples
##' set.seed(123)
##' require(ELMSurv)
##' require(survival)
##' #Lung DATA
##' data(lung)
##' lung=na.omit(lung)
##' lung[,3]=lung[,3]-1
##' n=dim(lung)[1]
##' L=sample(1:n,ceiling(n*0.5))
##' trset<-lung[L,]
##' teset<-lung[-L,]
##' rii=c(2,3)
##' # Default with lin_kernel
##' elmsurvmodel=cv.ELMCoxBAR(x=trset[,-rii],y=Surv(trset[,rii[1]], trset[,rii[2]]))
##' #The predicted linear predictor
##' bestpara=elmsurvmodel$Kernel_para
##' @export
cv.ELMCoxBAR <- function(x,y,Kernel_type="lin_kernel", Kernel_para=c(2,1),penality=0, maxiter=5,nfolds=2,...) {
if (!inherits(y, "Surv"))
stop("Response must be a 'survival' object - use the 'Surv()' function")
kplen=length(Kernel_para)
if(Kernel_type=="RBF_kernel"){
kerneltype=1
if(kplen==0||kplen<1){
stop("Error: Kernel Parameter for RBF_kernel Error!")
}
}else if(Kernel_type=="lin_kernel"){
kerneltype=2
if(kplen==0||kplen<1){
stop("Error: Kernel Parameter for lin_kernel Error!")
}
} else if(Kernel_type=="poly_kernel"){
kerneltype=3
if(kplen==0||kplen<2){
stop("Error: Kernel Parameter for poly_kernel Error!")
}
} else if(Kernel_type=="sigmoid_kernel"){
kerneltype=4
if(kplen==0||kplen<2){
stop("Error: Kernel Parameter for sigmoid_kernel Error!")
}
}else{
stop("Error:Unknow kernel types!")
}
maxc=Kernel_para[1];minc=maxc*0.01
cseq <- seq(maxc,minc,length.out=10)
nlambd=length(cseq)
#
folds <- cut(seq(1,nrow(x)),breaks=nfolds,labels=FALSE)
perffolds=rep(0,nlambd)
acc_temp=rep(0,nfolds)
#Perform 2 fold cross validation
for(k in 1:nfolds){
tryCatch({
for (i in 1:nlambd){
#Segement your data by fold using the which() function
testIndexes <- which(folds==k,arr.ind=TRUE)
tesetx <- x[testIndexes, ]
trsetx <- x[-testIndexes, ]
tesety <- y[testIndexes, ]
trsety <- y[-testIndexes, ]
#Use the test and train data partitions however you desire...
mycvbar=ELMCoxBAR(x=trsetx,y=trsety,Kernel_type, Kernel_para=c(cseq[i],minc),penality=penality, maxiter=maxiter)
#
perffolds[i]=mycvbar$elmcox$logLik
}
#best lambda
acc_temp[k]=cseq[which.min(perffolds)]
}, error=function(e){cat("ERROR :",conditionMessage(e), "\n")})
}
#message(perffolds)
bestc=mean(acc_temp)
fit <- list()
tryCatch({
fit = ELMCoxBAR(x,y, Kernel_type,Kernel_para=c(bestc,minc),penality, maxiter=5)
}, error=function(e){cat("ERROR :",conditionMessage(e), "\n")})
fit$trainx = x
fit$kerneltype = kerneltype
fit$Kernel_para = c(bestc,minc)
class(fit)="ELMCoxBAR"
fit
#best beta
}
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