Logistic quantile regression in Stata

Nicola Orsini and Matteo Bottai ()
Additional contact information
Matteo Bottai: University of South Carolina

Stata Journal, 2011, vol. 11, issue 3, 327-344

Abstract: We present a set of Stata commands for the estimation, prediction, and graphical representation of logistic quantile regression described by Bottai, Cai, and McKeown (2010, Statistics in Medicine 29: 309–317). Logistic quantile regression models the quantiles of outcome variables that take on values within a bounded, known interval, such as proportions (or percentages) within 0 and 1, school grades between 0 and 100 points, and visual analog scales between 0 and 10 cm. We describe the syntax of the new commands and illustrate their use with data from a large cohort of Swedish men on lower urinary tract symptoms measured on the international prostate symptom score, a widely accepted score bounded between 0 and 35. Copyright 2011 by StataCorp LP.

Keywords: lqreg; lqregpred; lqregplot; logistic quantile regression; robust regression; bounded outcomes (search for similar items in EconPapers)
Date: 2011
Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj11-3/st0231/
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

Downloads: (external link)
http://www.stata-journal.com/article.html?article=st0231 link to article purchase

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:tsj:stataj:v:11:y:2011:i:3:p:327-344

Ordering information: This journal article can be ordered from
http://www.stata-journal.com/subscription.html

Access Statistics for this article

Stata Journal is currently edited by Nicholas J. Cox and Stephen P. Jenkins

More articles in Stata Journal from StataCorp LLC
Bibliographic data for series maintained by Christopher F. Baum () and Lisa Gilmore ().