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st: New version of -haif- on SSC

From   "Roger B. Newson" <>
To   "" <>
Subject   st: New version of -haif- on SSC
Date   Thu, 16 May 2013 17:20:44 +0100

Thanks once again to Kit Baum, a new version of the -haif- package (superseding last week's version) is now available for download 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 adds new options -pweight(expression)- to the -haif- module, and new options -dpweight(expression)- and -npweight(expression)- to the -haifcomp- module, allowing the user to estimate the variance inflation caused by using sampling-probability weights, instead of (or as well as) by adding in covariates and/or confounding factors. I have also fixed a rarely-encountered bug, which caused -haifcomp- to ignore inverse-variance weights supplied by the user in order to make the variance inflation factor a heteroskedastic adjustment inflation factor assuming known variance ratios between observations. This bug fix has also been implemented in the old Stata Version 10 version of -haif-, which does not allow factor varlists, and which Stata Version 10 users can download from my website by typing, in Stata,

net from

and selecting and downloading -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()
      and/or by sampling probability weights specified by pweights().
      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, with sampling probability weights, to
      the corresponding variance (or standard error) of the same
      parameter, in a model containing only the core variables, without
      sampling probability weights.  These ratios are calculated
      assuming that the second model is true, and also assuming that
      the outcome variable is homoskedastic (equal-variance), or
      heteroskedastic with variance ratios inverse to the corresponding
      ratios of the inverse variance weights.  haifcomp calculates the
      ratios between the HAIFs for the same core variables caused by
      adjustment for two alternative lists of additional variables
      and/or sampling probability weights, namely a numerator list
      and/or weighting and a denominator list and/or weighting.  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: 10may2013
      Stata-Version: 11

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