R EVMIX convert pdf to uniform marginals - uniform

Question

I'm trying to convert a distribution into a pseudo-uniform distribution. Using the spd R package, it is easy and it works as expected.
library(spd)
x <- c(rnorm(100,-1,0.7),rnorm(100,3,1))
fit<-spdfit(x,upper=0.9,lower=0.1,tailfit="GPD", kernelfit="epanech")
uniformX = pspd(x,fit)
I want to generalize extreme value modeling to include threshold uncertainity. So I used the evmix package.
library(evmix)
x <- c(rnorm(100,-1,0.7),rnorm(100,3,1))
fit = fgkg(x, phiul = FALSE, phiur = FALSE, std.err = FALSE)
pgkg(x,fit$lambda, fit$ul, fit$sigmaul, fit$xil, fit$phiul, fit$ur,
fit$sigmaur, fit$xir, fit$phiur)
Im messing up somewhere.

Answer 1

Please check out the help for pgkg function:
help(pgkg)
which gives the syntax:
pgkg(q, kerncentres, lambda = NULL, ul = as.vector(quantile(kerncentres,
0.1)), sigmaul = sqrt(6 * var(kerncentres))/pi, xil = 0, phiul = TRUE,
ur = as.vector(quantile(kerncentres, 0.9)), sigmaur = sqrt(6 *
var(kerncentres))/pi, xir = 0, phiur = TRUE, bw = NULL,
kernel = "gaussian", lower.tail = TRUE)
You have missed the kernel centres (the data), which is always needed for kernel density estimators. Here is the corrected code:
library(evmix)
x <- c(rnorm(100,-1,0.7),rnorm(100,3,1))
fit = fgkg(x, phiul = FALSE, phiur = FALSE, std.err = FALSE)
prob = pgkg(x, x, fit$lambda, fit$ul, fit$sigmaul, fit$xil, fit$phiul,
fit$ur, fit$sigmaur, fit$xir, fit$phiur)
hist(prob) % now uniform as expected