# R/prrVal.r In pivotals: Functions implementing pivotal Monte Carlo methods

```prrVal<-function(x, Rsqr, S=10^4, model="w2", seed=1234, options=NULL, ProgRpt=FALSE)  {

## Test for valid x
if(length(x)==1) {
N = as.integer(x)
if (N < 3) {
stop("Insufficient data points")
}
event<-rep(1,N)
mranks<-mrank(event,options)
}else{
if(sum(x)<3) {
stop("Insufficient failure data points")
}else{
for(i in 1:length(x)) {
if(x[i]!=1&&x[i]!=0) {
stop("Not an event vector")
}
}
mranks<-mrank(x,options)
}
}

## Test for valid Rsqr
if(Rsqr<=0 || Rsqr>=1.0) 	stop("Invalid Rsqr")

## Test for valid S
S = as.integer(S/10)*10
if(S<10^3)  {
stop("Insufficient samples")
}
if(S>4*10^9)   {
stop("Samples beyond MAX_INT")
}

#seed=1234
Bval=.5   ## just to be some value, not used
CI=0.0  ## this disables Confidence band calculations
P1=1.0
P2=1.0

modeldf<-data.frame(model=model)

## Test for model
if(model=="w2") {

outdf<-.Call("pivotalMCw2p", mranks, c(Rsqr,CI,P1,P2), S, seed, Bval, ProgRpt, PACKAGE= "pivotals")
}else{

if(model=="ln2"|| model=="n") {

outdf<-.Call("pivotalMCln2p", mranks, c(Rsqr,CI,P1,P2), S, seed, Bval, ProgRpt, PACKAGE= "pivotals")
}else{
stop("model not recognized")
}
}
outdf<-cbind(outdf,modeldf)
outdf

}
```

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pivotals documentation built on May 2, 2019, 4:51 p.m.