Hello everyone:
I tried to fit a Beta distribution on a right-skewed dataset using:
fitdistr(temp,densfun="beta",start=list(shape1=3,shape2=2))
To assess the fit, I proceeded as follows:
Using distribution parameters from the sample resulting from fitdistr()
function, I generated 1000 samples as:
t <- rbeta(1000,3.0176976,6.0976797)
qqplot(temp, t)
It seems to be reasonable fit except in the tail.
I tried ks.test as:
ks.test(temp,"pbeta", 3.0176976,6.0976797)
One-sample Kolmogorov-Smirnov test
data: temp
D = 0.044, p-value < 2.2e-16
alternative hypothesis: two-sided
But, when I tried:
ks.test(t,temp)
Two-sample Kolmogorov-Smirnov test
data: t and temp
D = 0.0486, p-value = 0.02729
alternative hypothesis: two-sided
Would you please comment on my methodology? I suspect something is wrong.
ks.test results are confusing. I used sample parameters (resulting from
fitdistr()) to simulate data from assumed distribution, and then plotted
simulated data against my original data using qqplot...
I will appreciate any suggestions in this regard.
Thanks
Reez
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