Nothing
##' @export
`prop` <-
function(model,...) UseMethod("prop")
`prop.cph` <- function(model,
variable=NULL,
type.test=c("Lin"),
R=1000, plots=min(R,50), seed=NULL,
...) {
if(length(type.test)>1)
stop("Enter a test both")
if (is.null(model$x)==TRUE)
stop("The use of 'x=TRUE' for covariates design in cph model is required")
type.test.num=0
if(type.test=="Lin"){type.test.num=1}
if(type.test=="Liu"){type.test.num=2}
if(type.test.num==0)
stop("Enter a valid name for the test Lin or Liu")
if (is.null(seed)!=TRUE){set.seed(seed)}
seed=round(runif(1,1,1e9))
mt <- model.frame(model)
Y <- model.extract(mt, "response")
if (!inherits(Y, "Surv"))
stop("Response must be a survival object")
if (attr(Y, "type") == "right") {
time <- Y[, "time"];
status <- Y[, "status"]
} else stop("Expected right-censored data.");
X <- na.omit(model$x)
ot <- order(time); # order in time, status=1 first for ties
time <- time[ot]; status <- status[ot]
X <- X[ot,,drop=FALSE]
n <- length(time)
nd <- sum(status)
nc <- sum(status==0)
p <- ncol(X)
index.dtimes <- (1:n)[status==1]
dtimes <- time[index.dtimes]
index.censtimes <- (1:n)[status==0]
censtimes<- time[index.censtimes]
idxtime=which(time==time)
otime<-cbind(time,idxtime)
otime<-otime[!duplicated(otime[,1]),]
index.otime<-otime[,2]
otime<-otime[,1];
m=length(index.otime)
#Ties
if ((n>m)&(model$method!="breslow"))
warning("In case of ties, use breslow method in cph")
beta <- coef(model)
if(any(is.na(beta))) stop("Over-parametrized model")
if (is.null(variable)==FALSE){
if (length(variable)!=p) stop("Variables names must have same length than number of variables in model")}
if (is.null(variable)==TRUE){
variable <- na.omit(colnames(X))}
variable <- unique(variable)
UsedData <- X[,na.omit(match(variable, colnames(X))),drop=FALSE]
myvars <- variable
myvars.idx <- 1:p
output <- .C("coxscoreW",
R=as.integer(R),
n=as.integer(n),
m=as.integer(m),
nd=as.integer(nd),
nc=as.integer(nc),
p=as.integer(p),
seed=seed,
beta_data=as.double(beta),
time_data=as.double(time),
index_otime_data=as.integer(index.otime-1),
index_dtimes_data=as.integer(index.dtimes-1),
index_censtimes_data=as.integer(index.censtimes-1),
X_data=as.double(X),
plotnum=as.integer(plots),
type_test_num=as.integer(type.test.num),
KS=as.double(numeric(p)),
CvM=as.double(numeric(p)),
AS=as.double(numeric(p)),
Wsd=as.double(numeric(p*m)),
cvalues=as.double(numeric(p*R)),
Ws=as.double(numeric(p*m*plots)),
W=as.double(numeric(p*m)),
pkg="goftte"
)
UsedVars <- W <- Wsd <- What <- KS <- CvM <- AS <- allcvalues <- x <- mytype <- c()
mytype <- "prop"
KS=output$KS
CvM=output$CvM
AS=output$AS
W=array(output$W, dim=c(m,1,p))
What=array(output$Ws, dim=c(m,plots,p))
allcvalues=array(output$cvalues,dim=c(R,1,p))
Wsd=array(output$Wsd,dim=c(m,1,p))
x=array(rep(otime,p),dim=c(m,1,p))
res <- list(W=W, What=What,
obs=x,
KS=KS, CvM=CvM, AD=AS,
cvalues=allcvalues, variable=myvars,
R=R, sd=Wsd, type=mytype, model="cph", type.test=type.test,assumption="proportional hazards assumption")
class(res) <- "scproc"
res
}
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