Thanks to Kit Baum, a new package -haif- is now available for download from SSC. In Stata, use the -ssc- command to do this.
The -haif- package is described as below on my website. It is a tool for model selection, and calculates homoskedastic adjustment inflation factors (HAIFs) for the variances and standard errors of a core list of X-variables, caused by addition of some additional X-variables, assuming that these X-variables are unnecessary and that the outcome is homoskedastic. I don't know how often HAIFs have been invented before, or by what name they were called all those times. However, the -haif- package calculates them, lists them, and saves them in an output Stata matrix -r(haif)-.
Best wishes
Roger
Roger B Newson BSc MSc DPhil
Lecturer in Medical Statistics
Respiratory Epidemiology and Public Health Group
National Heart and Lung Institute
Imperial College London
Royal Brompton Campus
Room 33, Emmanuel Kaye Building
1B Manresa Road
London SW3 6LR
UNITED KINGDOM
Tel: +44 (0)20 7352 8121 ext 3381
Fax: +44 (0)20 7351 8322
Email: [email protected]
Web page: http://www.imperial.ac.uk/nhli/r.newson/
Departmental Web page:
http://www1.imperial.ac.uk/medicine/about/divisions/nhli/respiration/popgenetics/reph/
Opinions expressed are those of the author, not of the institution.
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package haif from http://www.imperial.ac.uk/nhli/r.newson/stata10
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TITLE
haif: Homoskedastic adjustment inflation factors for model selection
DESCRIPTION/AUTHOR(S)
haif calculates homoskedastic adjustment inflation factors (HAIFs)
for core variables in the corevarlist, caused by adjustment by the
additional variables specified by addvars(). HAIFs are calculated
for the variances and standard errors of estimated linear regression
parameters corresponding to the core variables. For each variance
(or standard error), the HAIF is defined as the ratio between that
variance (or standard error) of that parameter, in a model containing
both the core variables and the additional variables, to the
corresponding variance (or standard error) of the same parameter, in
a model containing only the core variables, calculated assuming that
the second model is true, and also assuming that the outcome variable
is homoskedastic (meaning that it has equal variances in all
subpopulations defined by the predictor variables). haifcomp
calculates the ratios between the HAIFs for the same core variables
caused by adjustment for two alternative lists of additional
variables, namely a numerator list and a denominator list. haif and
haifcomp are intended for use in model selection, allowing the user
to choose a model based on the joint distribution of the exposures
and confounders, before estimating the parameters of the model from
the data on the outcome variable.
Author: Roger Newson
Distribution-Date: 15march2009
Stata-Version: 10
INSTALLATION FILES (click here to install)
haif.ado
haifcomp.ado
haif.sthlp
haifcomp.sthlp
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