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st: New version of -haif- downloadable from SSC

From   "Newson, Roger B" <>
To   "''" <>
Subject   st: New version of -haif- downloadable from SSC
Date   Tue, 4 Aug 2009 17:38:34 +0100

Thanks to Kit Baum, a new version of the -haif- package is now downloadable from SSC. In Stata, use the -ssc- command to do this, or -adoupdate- if you already have an old version of -haif-.

The -haif- package is described as below on my website. The new version has been updated to Stata Version 11, and can now handle lists of core variables and additional variables containing factor variables. This new version is the planned subject of my planned presentation at the 2009 UK Stata User Meeting in London on 10-11 September, 2009, whose program is at

Users of Stata 10 can still download the old Stata 10 version of the -haif- package from my website by typing, in Stata,

net from "";

and selecting the Stata 10 -haif- package.

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: 04august2009
      Stata-Version: 11

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