New version of -parmest- on SSC

[Roger Newson]

Thanks as always to Kit Baum, a new version of the -parmest- package 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 -parmest-.

The -parmest- package is described as below on my website. The new version fixes a bug, which sometimes caused the -parmby- module to fail when processing very large datasets (eg tens of millions of observations). I would like to thank Tadashi Ito of the Graduate Institute in Geneva for alerting me to this bug.

Best wishes

Roger

--
Roger B Newson BSc MSc DPhil
Lecturer in Medical Statistics
Respiratory Epidemiology, Occupational Medicine
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 7594 7931
Email: [email protected]
Web page: http://www.imperial.ac.uk/nhli/r.newson/
Departmental Web page: http://www.imperial.ac.uk/nhli/reomph/

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

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package parmest from http://www.imperial.ac.uk/nhli/r.newson/stata11
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TITLE
parmest: Create datasets with 1 observation per estimated parameter

DESCRIPTION/AUTHOR(S)
The parmest package has 4 modules: parmest, parmby, parmcip and metaparm.
parmest creates an output dataset, with 1 observation per parameter of the
most recent estimation results, and variables corresponding to parameter names,
estimates, standard errors, z- or t-test statistics, P-values, confidence
limits and other parameter attributes. parmby is a quasi-byable extension to
parmest, which calls an estimation command, and creates a new dataset, with 1
observation per parameter if the by() option is unspecified, or 1 observation
per parameter per by-group if the by() option is specified. parmcip inputs
variables containing estimates, standard errors and (optionally) degrees of
freedom, and computes new variables containing confidence intervals and
P-values. metaparm inputs a parmest-type dataset with 1 observation for each
of a set of independently-estimated parameters, and outputs a dataset with
1 observation for each of a set of linear combinations of these parameters,
with confidence intervals and P-values, as for a meta-analysis. The output
datasets created by parmest, parmby or metaparm may be listed to the Stata
log and/or saved to a file and/or retained in memory (overwriting any
pre-existing dataset). The confidence intervals, P-values and other parameter
attributes in the dataset may be listed and/or plotted and/or tabulated.

Author: Roger Newson
Distribution-Date: 07april2014
Stata-Version: 11

INSTALLATION FILES (click here to install)
metaparm.ado
parmby.ado
parmcip.ado
parmest.ado
metaparm.sthlp
metaparm_content_opts.sthlp
metaparm_outdest_opts.sthlp
metaparm_resultssets.sthlp
parmby.sthlp
parmby_only_opts.sthlp
parmcip.sthlp
parmcip_opts.sthlp
parmest.sthlp
parmest_ci_opts.sthlp
parmest_outdest_opts.sthlp
parmest_resultssets.sthlp
parmest_varadd_opts.sthlp
parmest_varmod_opts.sthlp
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[Leigh]

Dear Dr. Newson,

Many thanks for your excellent program “parmest”, it has been a pleasure to use. One question regarding output of estimates using the –parmby- command. If I am running logistic regression and want the odds ratio estimates not the coefficients what is the proper way to execute that command and modify my code? The code I have been using is as follows (STATA 13):

*Regress over all vars on AD vs SCC
foreach v of varlist b_* {
local Xlab: var lab `v'
global modseq=$modseq+1
tempfile tf$modseq
parmby "xi: logistic diag_AD_SCC `v'", label command format(estimate min* max* %8.2f p %-8.2g) saving(`tf$modseq',replace) idnum($modseq) flist(tflist)
}
dsconcat $tflist
Thank you,
Leigh Greathouse

[Leigh]

Dear Dr. Newson,

Many thanks for your excellent program “parmest”, it has been a pleasure to use. One question regarding output of estimates using the –parmby- command. If I am running logistic regression and want the odds ratio estimates not the coefficients what is the proper way to execute that command and modify my code? The code I have been using is as follows (STATA 13):

*Regress over all vars on AD vs SCC
foreach v of varlist b_* {
local Xlab: var lab `v'
global modseq=$modseq+1
tempfile tf$modseq
parmby "xi: logistic diag_AD_SCC `v'", label command format(estimate min* max* %8.2f p %-8.2g) saving(`tf$modseq',replace) idnum($modseq) flist(tflist)
}
dsconcat $tflist
Thank you,
Leigh Greathouse