##generic functions
print.CE=function(object,...){
cat("\n")
cat("Call:\n")
print(object$call)
cat("\n")

  beta=object$beta
  var=object$var
  t=object$t
  y=object$y

  
  w=object$w

  varnames=object$varnames

  se=sqrt(diag(var))

  pv=2*(1-pnorm(abs(beta/se)))
  
  
cat("Proportional mean regression models for composite endpoint\n (Mao and Lin, 2016, Biostatistics):\n\n")

K=length(w)
weight=matrix(w,1,K)
rownames(weight)="Weight"
colnames(weight)=rep(" ",K)
colnames(weight)[1]="Event 1 (Death)"
for (k in 2:K){
colnames(weight)[k]=paste("Event",k)
}
print(weight)
cat("\n\n")
cat("Newton-Raphson algorithm converges in", object$i,"iterations.\n\n")


table=cbind(Estimate = beta,
             StdErr = se,
             z.value = beta/se,
             p.value = pv)

colnames(table)=c("Estimate","se","z.value","p.value")
rownames(table)=varnames

cat("Estimates for Regression parameters:\n\n")
printCoefmat(table, P.value=TRUE, has.Pvalue=TRUE)
cat("\n")
cat("\n")

za=qnorm(0.975)
MR=cbind(exp(beta),exp(beta-za*se),exp(beta+za*se))
colnames(MR)=c("Mean Ratio","95% lower CL","95% higher CL")
rownames(MR)=varnames
cat("Point and interval estimates for for mean ratios:\n\n")
print(MR)
cat("\n")

}




plot.CE=function(object,z,xlab="Time",
ylab="Mean function",lty=1,frame.plot=F,add=F,
...){
  
  beta=object$beta
  var=object$var
  t=object$t
  y=object$y


  
  mf=exp(sum(beta*z))*y

  

  if (add==F){
	plot(stepfun(t,c(0,mf)),do.point=F, 
		lty=lty,xlab=xlab,ylab=ylab,frame.plot=frame.plot,...)
  }else{
    lines(stepfun(t,c(0,mf)),do.point=F,lty=lty,...)
  }
  }