iprlma178
/opt/cluster/R/3.0.2/bin:/opt/cluster/openmpi/gcc/64/1.4.2/bin:/opt/cluster/gcc/4.6.0/bin:/sbin:/usr/sbin:/bin:/usr/bin:/usr/local/bin:/opt/cluster/bin

R version 3.0.2 (2013-09-25) -- "Frisbee Sailing"
Copyright (C) 2013 The R Foundation for Statistical Computing
Platform: x86_64-unknown-linux-gnu (64-bit)

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> library(copula)
> data(loss)
> myLoss <- subset(loss, censored==0, select=c("loss", "alae"))
> 
> n <- nrow(myLoss)
> 
> ## values of the marginal empirical c.d.f.s at observations times n
> mr.loss <- rank(myLoss[,1], ties.method="max")
> mr.alae <- rank(sort(myLoss[,2]), ties.method="max")
> 
> tau <- cor(myLoss[,1], myLoss[,2], method="kendall")
> theta <- iTau(gumbelCopula(), tau)
> 
> test.func <- function(x) evTestC(x)$p.value
> 
> ## one replication
> do1 <- function()
+ {
+     x <- rCopula(n, gumbelCopula(theta))
+     y <- x[order(x[,1]),] ## sort according to first coordinate
+     y[,1] <- y[mr.loss,1] ## match ties structure in LOSS
+     # stopifnot(all(mr.loss == rank(y[,1], ties.method="max")))
+     y <- y[order(y[,2]),] ## sort according to second coordinate
+     y[,2] <- y[mr.alae,2] ## match ties structure in ALAE
+     # stopifnot(all(mr.alae == rank(y[,2], ties.method="max")))
+     z <- apply(y, 2, rank, ties.method="random")
+     ## apply test on continuous, tied and randomized sample
+     c(test.func(x), test.func(y), test.func(z))
+ }
> 
> res <- t(replicate(100,do1()))
> write.table(res,"pvalsC.csv")
> summary(res[,1] - res[,3])
      Min.    1st Qu.     Median       Mean    3rd Qu.       Max. 
-0.1039000 -0.0142400 -0.0014990 -0.0004995  0.0164800  0.0869100 
> summary(res[,1] - res[,2])
    Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
-0.01898  0.26970  0.48150  0.45360  0.65960  0.87310 
> apply(res,2, function(x) mean(x <= 0.05))
[1] 0.05 0.45 0.05
>