# How to construct Kolmogorov Smirnov test to check which distribution fits to given data in R

So, if for example I generate random sample of 1000 from exponential distribution and then simulate ks.test. for many times I get a normally distributed data, here is the code:

ks_test <-  function(n){
df <-  rexp(n)
st <-  sqrt(n)*(ks.test(izlase, "pexp", 1/mean(df))$statistic) return(st) } n <- 1000 N <- 10000 ks_stat <- replicate(N,ks_test(n)) hist(ks_stat, breaks = 25, col = "orange", prob = TRUE) lines(density(ks_stat),col = "black", lwd = 2)  But if I have a sample and I would like to see which distribution the data fits, I run into problem while creating the function. Here is the code I tried: ks_test <- function(n){ st <- sqrt(n)*(ks.test(df$$x, "pexp", 1/mean(df$$x))$statistic)
return(st)
}
n <-  1000
N <- 10000
ks_stat <-  replicate(N,ks_test(n))
hist(ks_stat, breaks = 25, col = "orange", prob = TRUE)
lines(density(ks_stat),col = "black", lwd = 2)


I understand that the ks_stat gives the same number each time in this case, but I don't know what to do in case I need to check for the distribution if the only given is a sample of size 32. Anybody knows, how to construct the ks_test for numerous distribution to check if the data correspond to the distribution?

• I remember that it is imperative for the Original source document to contain the CATEGORICAL SPECIFICS that ultimately will provide such successfully derived results. \\^..^// – Kti Pne Jun 17 at 0:38