##' Grow a rotation survival random forest
##' @title sfpls rostf_pca
##' @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 testx The covariates(predictor variables) of test data.
##' @param mtry The number of covariates(predictor variables) used in each base ELM model. Default is the square root of the number of all avaibable covariates.
##' @param trlength The ensemle size (the number of base ELM survival models). Default is 100.
##' @param Regularization_coefficient Ridge or Tikhonov regularization parameter. Default is 10000. Also known as \eqn{C} in the ELM paper.
##' @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)"
##' @return Object of class \code{ELMSurvEN} with elements
##' \tabular{ll}{
##' \code{elmsurvfit} \tab A list of base models \code{elm_surv} of size \code{trlength}. To retrieve a particular base model: use elmsurvfit[[i]], where i takes values between 1 and \code{trlength} \cr
##' \code{precitedtime} \tab Esitmated survival times of test data. \cr
##' }
##' @seealso \code{\link{elm_surv}}
##' @author Hong Wang
##' @references
##' \itemize{
##' \item Zhou L, Xu Q, Wang H. (2015) Rotation survival forest for right censored data. PeerJ 3:e1009 https://doi.org/10.7717/peerj.1009.
##' }
##' @examples
##' set.seed(123)
##' require(ELMSurv)
##' require(survival)
##' ## Survival Ensemble of ELM with default settings
##' #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)
##' elmsurvmodel=ELMSurvEN(x=trset[,-rii],y=Surv(trset[,rii[1]], trset[,rii[2]]),testx=teset[,-c(rii)])
##' testpretimes=elmsurvmodel$precitedtime
##' #The predicted survival times on the first test example
##' head(testpretimes[1,])
##' #The predicted survival times of all test examples by the third model
##' head(testpretimes[,3])
##' # Get the 1th base model
##' firstbasemodel=elmsurvmodel$elmsurvfit[[1]]
##' @export
rotsfpca <-
function (formula, data, trlength=500,m=2,control = control, na.action = na.omit,vari_status=FALSE)
{
Call <- match.call()
data=na.omit(data)
mf <- model.frame(formula, data)
x <- model.matrix(attr(mf, "terms"), data = mf)
y <- model.response(mf)
if (!inherits(y, "Surv"))
stop("Response must be a 'survival' object - use the 'Surv()' function")
ny <- ncol(y)
n <- nrow(y)
status <- y[, ny]
survtime=y[, 1L]
if (any(survtime <= 0)) stop("Observation time must be > 0")
if (all(status == 0)) stop("No deaths in training data set")
if (!missing(control))
controls[names(control)] <- control
#names(data)=c("time","status",names(x))
p=dim(mf)[2]
rotms<- vector(mode = "list", length = trlength)
pectrees <- vector(mode = "list", length = trlength)
varimp<-NULL
for (i in 1:trlength)
{
#create a bagging version for rotation
trainindex=sample(nrow(mf),replace=T)
trsetold=mf[trainindex,]
# oobset
train_posp<-1:nrow(mf) %in% trainindex
oobset=mf[!train_posp,]
# rotation for gr i bagging version
pp=c(1:p)
#excluding reponse index:
rii=c(1)
#the rest predictors
pp=setdiff(pp,rii)
#how many groups in the rotation
rp=p-length(rii)
gr=(rp)/m
#initialize varimportance
varimport=c(rep(0,rp))
#initialize the rotation matrix
rotationm=matrix(rep(0,(rp)^2),nrow=rp,ncol=rp)
for (j in 1:gr)
{
trainindex=sample(nrow(trsetold),replace=T)
trainb=trsetold[trainindex,]
d=sample(pp,m)
olddata=trsetold[trainindex,d]
#for pca
rotj=prcomp(olddata)
for (jj in 1:m) {
#for pca
rotationm[d-length(rii),(j-1)*m+jj]=rotj$rotation[,jj]
}
pp=setdiff(pp,d)
}
dd=rotationm
# those features not used in the current pca is set to 0
rotationm[pp-length(rii),]=0
# the final the rotation matrix
rotms[[i]]=rotationm
# the final bagging training set
rmatrix=as.matrix(trsetold[,-c(rii)]) %*% t(rotationm)
rmatrixnew=as.data.frame(rmatrix)
rmatrixnew=data.frame(trsetold[,rii][,1],trsetold[,rii][,2],rmatrixnew)
colnames(rmatrixnew)[c(1,2)]=c("time","status")
colnames(rmatrixnew)[-c(1,2)]=colnames(trsetold)[-c(1)]
#trees[[i]]=rpart(rmatrixnew,control = control)
#print(head(rmatrixnew))
pectrees[[i]]=pecRpart(formula,data=rmatrixnew)
if (vari_status)
{
# preparing for predicting oob before permutation
rmatrix0=as.matrix(oobset[,-c(rii)]) %*% t(rotationm)
rmatrixnew0=as.data.frame(rmatrix0)
rmatrixnew0=cbind(oobset[,rii],rmatrixnew0)
colnames(rmatrixnew0)=colnames(oobset)
predict_oob<-predict(trees[[i]],rmatrixnew0[,-c(rii)])
ci_before=concordance.index(predict_oob,oobset$time,oobset$status)
# CAL c-index DECREASE
#test first covariate
X=cbind(sample(oobset[,3]),oobset[,c(4:p)])
rmatrix1=as.matrix(X) %*% t(rotationm)
rmatrixnew0=as.data.frame(rmatrix1)
rmatrixnew0=cbind(oobset[,rii],rmatrixnew0)
colnames(rmatrixnew0)=colnames(oobset)
predict_oob1<-predict(trees[[i]],rmatrixnew0[,-c(rii)])
ci_after=concordance.index(predict_oob1,oobset$time,oobset$status)
varimport[1]=unlist(ci_before[1])-unlist(ci_after[1])
#test last column
X=cbind(oobset[,3:(p-1)],sample(oobset[,p]))
rmatrix1=as.matrix(X) %*% t(rotationm)
rmatrixnew0=as.data.frame(rmatrix1)
rmatrixnew0=cbind(oobset[,rii],rmatrixnew0)
colnames(rmatrixnew0)=colnames(oobset)
predict_oob1<-predict(trees[[i]],rmatrixnew0[,-c(rii)])
ci_after=concordance.index(predict_oob1,oobset$time,oobset$status)
varimport[p-2]=unlist(ci_before[1])-unlist(ci_after[1])
#test others
for (jjj in 4:(p-1)) {
X=cbind(oobset[,3:(jjj-1)],sample(oobset[,jjj]),oobset[,(jjj+1):p])
rmatrix1=as.matrix(X) %*% t(rotationm)
rmatrixnew0=as.data.frame(rmatrix1)
rmatrixnew0=cbind(oobset[,rii],rmatrixnew0)
colnames(rmatrixnew0)=colnames(oobset)
predict_oob1<-predict(trees[[i]],rmatrixnew0[,-c(rii)])
ci_after=concordance.index(predict_oob1,oobset$time,oobset$status)
varimport[jjj-2]=unlist(ci_before[1])-unlist(ci_after[1])
}
if (vari_status) { varimp=cbind(varimp,varimport) }
}
}
fit=pectrees
if (vari_status)
{
vari=rowMeans(varimp)
#make the difference apparent
vari=vari*trlength
colnames(vari)=colnames(mf[,-c(rii)])
}
class(fit) <- "rotsf"
if (vari_status)
return(list(pectrees=pectrees,rotms=rotms,rii=rii,vari=vari))
else
return(list(pectrees=pectrees,rotms=rotms,rii=rii))
}
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