MLE_SMLE_script.R

    library(Rcpp) 
    icm<-sourceCpp("icm.cpp")
    
    A<-read.table("dataThai.txt")
    output <- ComputeMLE(A)
    
    x1 <- output$MLE[,1:4]
    x2 <- output$SMLE[,1:4]
        
    plot(c(-100,-100),xlim=c(min(x2[,1]),max(x2[,1])), ylim=c(0,max(x1[,2:4],x2[,2:4])),main= "",ylab="",xlab="",bty="n",las=1)
    lines(x1[,1], x1[,2],type="s",col="blue")
    lines(x1[,1], x1[,3],type="s",lty=2,col="red")
    lines(x1[,1], x1[,4],type="s",lty=3)
    lines(x2[,1], x2[,2])
    lines(x2[,1], x2[,3])
    lines(x2[,1], x2[,4])

    A<-read.table("data25000.txt")
    output <- ComputeMLE(A)
    
    x1 <- output$MLE[,1:4]
    x2 <- output$SMLE[,1:4]
    
    plot(c(-100,-100),xlim=c(min(x2[,1]),max(x2[,1])), ylim=c(0,max(x1[,2:4],x2[,2:4])),main= "",ylab="",xlab="",bty="n",las=1)
    lines(x1[,1], x1[,2],type="s",col="blue")
    lines(x1[,1], x1[,3],type="s",lty=2,col="red")
    lines(x1[,1], x1[,4],type="s",lty=3)
    lines(x2[,1], x2[,2])
    lines(x2[,1], x2[,3])
    lines(x2[,1], x2[,4])

#   save.image("comprisk.RData")