# Fitting distributions with R

Fitting distribution with R is something I have to do once in a while, but where do I start?

A good starting point to learn more about distribution fitting with R is Vito Ricci's tutorial on CRAN. I also find the vignettes of the actuar and fitdistrplus package a good read. I haven't looked into the recently published Handbook of fitting statistical distributions with R, by Z. Karian and E.J. Dudewicz, but it might be worthwhile in certain cases, see Xi'An's review. A more comprehensive overview of the various R packages is given by the CRAN Task View: Probability Distributions, maintained by Christophe Dutang.

How do I decide which distribution might be a good starting point?

I came across the paper Probabilistic approaches to risk by Aswath Damodaran. In Appendix 6.1 Aswath discusses the key characteristics of the most common distributions and in Figure 6A.15 he provides a decision tree diagram for choosing a distribution:

 Figure 6A.15 from Probabilistic approaches to risk by Aswath Damodaran

JD Long points to the Clickable diagram of distribution relationships by John Cook in his blog entry about Fitting distribution X to data from distribution Y . With those two charts I find it not too difficult anymore to find a reasonable starting point.

Once I have decided which distribution might be a good fit I start usually with the fitdistr function of the MASS package. However, since I discovered the fitdistrplus package I have become very fond of the fitdist function, as it comes with a wonderful plot method. It plots an empirical histogram with a theoretical density curve, a QQ and PP-plot and the empirical cumulative distribution with the theoretical distribution. Further, the package provides also goodness of fit tests via gofstat.

Suppose I have only 50 data points, of which I believe that they follow a log-normal distribution. How much variance can I expect? Well, let's experiment. I draw 50 random numbers from a log-normal distribution, fit the distribution to the sample data and repeat the exercise 50 times and plot the results using the plot function of the fitdistrplus package.

I notice quite a big variance in the results. For some samples other distributions, e.g. logistic, could provide a better fit. You might argue that 50 data points is not a lot of data, but in real life it often is, and hence this little example already shows me that fitting a distribution to data is not just about applying an algorithm, but requires a sound understanding of the process which generated the data as well.

1. Mohan Radhakrishnan8 May 2012 at 11:58

What is a good book or paper to read about the practical utility of data fitting ? I find it  easier to use R than to get at the use of this. Is this used to compare data sets from different load tests ?

2. I am sorry, but I don't understand your question. The above post points you to some papers which explain distributions fitting in more detail.

3. Mohan Radhakrishnan9 May 2012 at 07:07

I was looking at some capacity planning issues and know of various good books. So I was trying to understand what kind of statistical analysis of future workload will result from the effort to find what distribution the data models. I use R. I think I am missing the data analysis part.
it ?

4. I love that decision tree, thanks for posting. What do you recommend doing if you have a column of scores and have no clue what the distribution is? What's steps do you take to determine what it might be? I need to do this to two columns so that I can determine how to determine correlation between them.

5. Plot the data to understand what the distribution looks like.

6. Thanks for your reply Markus. So after I remove outliers from the source data you suggest I do a qq plot and/or a histogram in R first? Then do something like above to confirm what I inferred by the plot?

7. very helpful article. Thanks a million