Generalized ordered logit/partial proportional odds models for ordinal dependent variables
This article describes the gologit2 program for generalized ordered logit models. gologit2 is inspired by Vincent Fu’s gologit routine (Stata Technical Bulletin Reprints 8: 160–164) and is backward compatible with it but offers several additional powerful options. A major strength of gologit2 is that it can fit three special cases of the generalized model: the proportional odds/parallel-lines model, the partial proportional odds model, and the logistic regression model. Hence, gologit2 can fit models that are less restrictive than the parallel-lines models fitted by ologit (whose assumptions are often violated) but more parsimonious and interpretable than those fitted by a nonordinal method, such as multinomial logistic regression (i.e., mlogit). Other key advantages of gologit2 include support for linear constraints, survey data estimation, and the computation of estimated probabilities via the predict command.
Volume (Year): 6 (2006)
Issue (Month): 1 (March)
|Contact details of provider:|| Web page: http://www.stata-journal.com/|
|Order Information:||Web: http://www.stata-journal.com/subscription.html|
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Rory Wolfe & William Gould, 1998. "An approximate likelihood-ratio test for ordinal response models," Stata Technical Bulletin, StataCorp LP, vol. 7(42).
When requesting a correction, please mention this item's handle: RePEc:tsj:stataj:v:6:y:2006:i:1:p:58-82. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Christopher F. Baum)or (Lisa Gilmore)
If references are entirely missing, you can add them using this form.