R/prop.cph.R

In goftte: Goodness-of-Fit for Time-to-Event Data

##' @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
}