Notice: On April 23, 2014, Statalist moved from an email list to a forum, based at statalist.org.
From | Maarten buis <maartenbuis@yahoo.co.uk> |
To | stata list <statalist@hsphsun2.harvard.edu> |
Subject | st: update betafit available from SSC |
Date | Fri, 8 Apr 2011 16:10:50 +0100 (BST) |
Thanks to Kit Baum an update of the -betafit- package is now available from SSC. To install type in Stata: -ssc install betafit, replace- or -adoupdate, update-. -betafit- fits by maximum likelihood a beta- distribution with or without covariates to data. One of the key characteristics of the the beta- distribution is that it is a fairly flexible distribution for variables that are bounded between 0 and 1. It can thus be used to model proportions or other bounded variables, and optionally how they depend on explanatory/ independent/right-hand-side/x variables. This update contains the following changes: o After estimating a -betafit- model one can predict 3 new types of residuals: working residuals, partial residuals, and score residuals. o For users who have Stata 11 or higher: -betafit- allows factor variables and with that also the use of -margins- as a post-estimation tool. o -betafit- can display results as "relative proportion ratios", that is, as exponentiated coefficients. A discussion on relative proportion ratios is included in the help file. -betafit- has a relatively long history with input from various authors. In 1997 Nick Cox wrote a -beta- for fitting a beta distribution by ML without covariates. This program is still part of -betafit- as -beta4-, and it should work with Stata 4 upwards. This didn't use -ml-: it was a fairly straight port of an algorithm published in Journal of Applied Meteorology by P.W. Mielke in 1975. In 2003, riding on the back of some work by Stephen Jenkins on other distributions, Nick Cox wrote a new -betafit- that now took covariates if specified. But it stuck to the parameterisation of a beta distribution in terms of two shape parameters. Every now and again threads on Statalist ask what should be done when the response is a continuous proportion between 0 and 1, and sooner or later someone mentions the beta distribution as one possibility, but that that the then existing parameterisation of -betafit- was not particularly useful for problems with covariates. In 2005 I came on board, taking the initiative in implementing an extra parameterisation in terms of a location and a scale parameter. Since then I have initiated various extensions to the program like adding capabilities to compute marginal effects (2006), allow the use of -predict- (2008), and the latest update that became available today. -betafit- is now attributable to Buis, Cox, and Jenkins. I hope that some of you will find it useful, Maarten -------------------------- Maarten L. Buis Institut fuer Soziologie Universitaet Tuebingen Wilhelmstrasse 36 72074 Tuebingen Germany http://www.maartenbuis.nl -------------------------- * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/