I have fitted Hyperexponential distribution (HED) and Hypoexponential
distribution (HoED) to two different data sets (of size 1000 numeric values
each) using a software package called EMpht.
I want to use R to perform goodness-of-fit test for the fitted distribution
with respect to the empirical CDFs of the data sets using KS test
(Kolmogorov-Smirnov test).
ks.test() function in R takes the first argument as the data set,
and the second argument as the name of the distribution, followed by the
distribution parameter values. In case of the CDFs that are already supported
by R, this is simple (for example: ks.test(data_set, "pnorm", mean, sd)).
1. Can somebody please suggest whether R has in-built support for
Hyperexponential and Hypoexponential CDFs
(they do not appear in the list of distribution given in "An Introduction to
R"))?
2. If I write an R function to compute HED (or HoED) CDF value, can I use that
function name as second argument in ks.test()? For example, if I implement an R
function named HED_CDF with parameters <parameters...>, will it be correct to
use ks.test() as follows:
ks.test(data_set, "HED_CDF", <parameters...>)
Will it give correct result?
- Manuj Sharma
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