# R/checkresults.R In PHInfiniteEstimates: Tools for Inference in the Presence of a Monotone Likelihood

#### Documented in checkresults

```#' Produce a graphical assessment of Monte Carlo experiment on fidelity of proportional hazards regression to the uniform ideal.
#'
#' This function draws a quantile plot for Monte Carlo assessments of fit to the corrected proportional hazards fit.
#' @param regnsimulation A matrix with six columns and as many rows as there MC samples.
#' @export
##' @importFrom stats update
#' @importFrom graphics lines
#' @importFrom stats dnorm pchisq
#' @return A list with components of consisting of simulated Wald p-values, likelihood ratio p-values, and corrected likelihood ratio p-values.
checkresults<-function(regnsimulation){
hw<-regnsimulation[,"SRLRT"]
hz<-regnsimulation[,"Est"]/regnsimulation[,"SE"]
ospvb<-pnorm(hw)+(1/hw-1/hz)*dnorm(hw)
flip<-ospvb<0
flip[is.na(flip)]<-FALSE
ospvb[flip]<-0
flip<-ospvb>1
flip[is.na(flip)]<-FALSE
ospvb[flip]<-1
tspv<-ospvb
flip<-tspv>.5
flip[is.na(flip)]<-FALSE
tspv[flip]<-1-tspv[flip]
tspv<-2*tspv
uu<-sort(tspv)
ss<-sort(regnsimulation[,1])
tt<-sort(regnsimulation[,2])
use<-seq(length(ss)/10)
plot(use/length(ss),ss[use],type="l")
use<-seq(length(tt)/10)
lines(use/length(tt),tt[use],lty=2)
use<-seq(length(uu)/10)
lines(seq(use)/length(uu),uu[use],lty=3)
legend(0,max(ss[use]), lty=1:3,