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st: New package -haif- on SSC

From   "Newson, Roger B" <>
To   statalist <>
Subject   st: New package -haif- on SSC
Date   Mon, 16 Mar 2009 14:03:15 +0000

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 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
Tel: +44 (0)20 7352 8121 ext 3381
Fax: +44 (0)20 7351 8322
Web page:
Departmental Web page:

Opinions expressed are those of the author, not of the institution.

package haif from

      haif: Homoskedastic adjustment inflation factors for model selection

      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

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