Logistic quantile regression in Stata
Nicola Orsini
Unit of Biostatistics
and
Unit of Nutritional Epidemiology
Institute of Environmental Medicine, Karolinska Institutet
Stockholm, Sweden
[email protected]
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Matteo Bottai
Division of Biostatistics
University of South Carolina
Columbia, SC
and
Unit of Biostatistics
Institute of Environmental Medicine, Karolinska Institutet
Stockholm, Sweden
[email protected]
|
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.
View all articles by these authors:
Nicola Orsini, Matteo Bottai
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lqreg, lqregpred, lqregplot, logistic quantile regression, robust regression, bounded outcomes
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