R Package fitdistrplus – How to Use for Distribution Fitting and Goodness-of-Fit

distributionsgoodness of fitr

How can I find all distribution that fitdistrplus package support. I find some examples from what I discovered what is required distr name, few example: if I am looking for log normal distribuiton distr name is "lnorm", if I am trying to fit Chi-Square Distribution then name for distr is "chi-squared", and so on. Is there some where in package manual where I can find all suported distributions.
I am interested in distributions that support positive integer values with big variance and relatively small mean (right skewed). Cullen and Frey graph suggested log normal distribution, data AIC and BIC for log normal model are the lowest but KS.test don t support this model.So I am trying to fit to other model but how to find right name for "dist".

Best Answer

The fitdistrplus FAQ claims to fit distributions for which d, p, q and r functions are available, whether in base R or a package or which you have written yourself (though in more obscure ones you will need to provide sensible start values).

Common distribution names such as chisq can be found in the R-intro manual

Related Solutions

Solved – the best way to perform a goodness-of-fit test of data to a continuous distribution with the chi-square method in R

It's a pity that a chi-square goodness-of-fit test is a "project requirement," because in general there is much to be lost by binning continuous variables. If possible, try to convince those in charge to allow methods more appropriate for continuous variables in this context of distribution fitting.

If you are stuck with binning you might have a problem. Essentially, if you define the bin boundaries based on parameters of a distribution estimated from the data, then the chi-square test itself may no longer be valid. This is beyond the simple loss of degrees of freedom in chi-square due to estimating parameters; the chi-square statistic as usually calculated may be no longer distributed as chi-square. See the "Level 3" section of the answer by @cardinal on that linked page; that answer includes some recommendations for how to proceed (which are beyond my particular expertise). As you proceed, you do have to consider what you are trying to accomplish by fitting different types of distributions to the same data, and to recognize that results might be hard to generalize even if you find a reliable way to define useful bin boundaries.

Solved – fitting a log-logistic distribution

I am not sure I understand your problem, but this can be done directly with fitdistr. See the following code:

library(fitdistrplus)
library(actuar)

test <- actuar::rllogis(500, 3.3, 1)
fitdist(test, "llogis")
Fitting of the distribution ' llogis ' by maximum likelihood 
Parameters:
      estimate Std. Error
shape 3.374404 0.12585423
scale 0.998631 0.02296057

The fitdistr function do not have a set of predefined distribution models, it just assumes that pdqr... functions corresponding to basename llogis exist somewhere on the search path.

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Solved – fitting a log-logistic distribution

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