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When using the GAMLSS package in R, there are many different ways to fit a distribution to a set of data. My data is a single vector of values, and I am fitting a distribution over these values.

My question is this: what is the main difference between using fitDist() and gamlss() since they give similar but different answers for parameter values, and different worm plots?

Also, using the function confint() works for gamlss() fitted objects but not for objects fitted with fitDist(). Is there any way to produce confidence intervals for parameters fitted with the fitDist() function? Is there an accuracy difference between the two procedures? Thanks!

redfish993
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m1 <- fitDist()

fits many distributions and chooses the best according to a generalized Akaike information criterion, GAIC(k), wit penalty k for each fitted parameter in the distribution, where k is specified by the user, e.g. k=2 for AIC, k = log(n) for BIC, k=4 for a Chi-squared test (rounded from 3.84, the 5% critical value of a Chi-squared distribution with 1 degree of fereedom), which is my preference.

m1$fits

gives the full results from the best to worst distribution according to GAIC(k).

Robert
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