Nothing
npr.wpc.est <-
function(event,censor,marker,cutoff,method,weights,wdth=0,nsub=0,sspeed,df=2,confi="NO",nbtsp=1000,quantile=0.95)
{ if(method == "window.width") {summary = ww.windows(event,censor,marker,wdth,sspeed)}
if(method == "number.subjt") {summary = ns.windows(event,censor,marker,nsub,sspeed)}
fit = vector("list",summary$ntotal); x = y = X = Y = s = NA
for(i in 1:summary$ntotal)
{ if(summary$nsam[i]!=0)
{ if(weights == "uniform" || summary$winsize[i]==0)
{ wts = rep(1,summary$nsam[i]) }
if(weights == "normal" && summary$winsize[i]!=0)
{ wts = dnorm(x=summary$wdata[[i]]$marker , mean=summary$xwin[i] , sd=summary$winsize[i]/8) }
if(weights == "trunnormal"&& summary$winsize[i]!=0)
{ wts = dtnorm(summary$wdata[[i]]$marker , summary$xwin[i] , sd=summary$winsize[i]/8 ,
summary$xwin[i]-summary$winsize[i]/8 , summary$xwin[i]+summary$winsize[i]/8) }
if(weights == "huber" && summary$winsize[i]!=0)
{ wts = ifelse(abs(summary$wdata[[i]]$marker-summary$xwin[i]) <= summary$winsize[i]/8, 1,
summary$winsize[i]/8/abs(summary$wdata[[i]]$marker-summary$xwin[i])) }
fit[[i]] = surv.rate(summary$wdata[[i]] , cutoff , wts , summary$xwin[i])
x[i] = fit[[i]]$x
y[i] = fit[[i]]$y }
else{ x[i] = NA ; y[i]=NA} }
Y = na.omit(y)
X = na.omit(x)
s = predict(loess(Y ~ X,df = df))
s[which(s<=0)]=0; s[which(s>=1)]=1
if(confi=="NO"){ return(list(x=X,y=Y,s=s)) }
if(confi == "YES"){ n = length(marker)
nxwin = length(x)
data = data.frame(event,censor,marker)
REboot =REcurve= vector("list",nbtsp)
REmatrix = matrix(NA,nxwin,nbtsp)
sw.lower = sw.upper=slb=sub =lb=ub= NA
for(j in 1:nbtsp)
{ print(j)
cur.ix = sample(1:n,n,replace=TRUE)
D = data[cur.ix,]
REboot[[j]] = npr.wpc.est(D$event,D$censor,D$marker,cutoff,method,weights,wdth,nsub,sspeed,df)
REcurve[[j]] = cbind (REboot[[j]]$x,REboot[[j]]$s)
for(i in 1:nxwin){if(length(which(REcurve[[j]][,1]==x[i])>0)){REmatrix[i,j]=REcurve[[j]][min(which(REcurve[[j]][,1]==x[i])),2]}} }
for(i in 1:nxwin)
{ quant = quantile(na.omit(REmatrix[i,]),c(1-quantile,quantile))
sw.lower[i] = max(0,quant[1])
sw.upper[i] = min(1,quant[2]) }
slb=na.omit(sw.lower)
sub=na.omit(sw.upper)
lb = predict(loess(slb ~ X,df = df)); lb[which(lb<=0)]=0; lb[which(lb>=1)]=1
ub = predict(loess(sub ~ X,df = df)); ub[which(ub<=0)]=0; ub[which(ub>=1)]=1
return(list(x=X,y=Y,s=s,lb=lb,ub=ub)) }
}
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