Sorry, the phrase
It can therefore now do -qual-variance t-tests as well as Satterthwaite
unequal-variance t-tests.
should of course read
It can therefore now do equal-variance t-tests as well as Satterthwaite
unequal-variance t-tests.
Best wishes
Roger
Roger Newson
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]
www.imperial.ac.uk/nhli/r.newson/
Opinions expressed are those of the author, not of the institution.
-----Original Message-----
From: [email protected]
[mailto:[email protected]] On Behalf Of Newson, Roger
B
Sent: 01 December 2006 12:11
To: [email protected]
Subject: st: New version of -parmest- (incorporating -metaparm-) on SSC
Hello All
Thanks to Kit Baum, the first Stata 9 version of the -parmest- package
(now merged with the -metaparm- package) is now available for download
on SSC. In Stata, use the -ssc- command to do this.
The -parmest- package is described as below on my website. The new
version adds the following improvements:
1. The -parmest- package is now merged with the -metaparm- package.
2. The precision of the -metaparm- and -parmcip- modules has been
upgraded to that of -parmest- by doing all calculations initially in
double precision, whether or not the -float- option is used.
3. The -parmcip- module now has a -replace- option, allowing it to
overwrite existing variables with the same names as its output
variables.
3. The -metaparm- module now has a -dfcombine()- option for specifying
ways of combining input degrees of freedom to derive output degrees of
freedom. It can therefore now do -qual-variance t-tests as well as
Satterthwaite unequal-variance t-tests.
4. However, the change that most users will notice is that the online
help has been upgraded. The user can therefore now see, at a glance, the
wide range of options available, which are listed and classified in
tables in the primary help files, and may optionally refer to secondary
help files to look up the technical details. This change is in line with
Stata 9 practice for large comprehensive packages (such as -graph
twoway-), and uses Stata 9 SMCL. It is intended to make -parmest- easier
to use, but I would greatly appreciate any feedback on this.
I would like to thank the reviewers of my article in the latest issue of
The Stata Journal (out yesterday in its online form) for alerting me to
the capabilities of Stata 9 SMCL, and for demonstrating them to me by
upgrading the on-line help for my -somersd- package, which gave me the
idea for doing something similar for -parmest-.
Best wishes
Roger
Roger Newson
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]
www.imperial.ac.uk/nhli/r.newson/
Opinions expressed are those of the author, not of the institution.
------------------------------------------------------------------------
-------------------------
package parmest from http://www.imperial.ac.uk/nhli/r.newson/stata9
------------------------------------------------------------------------
-------------------------
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: 28november2006
Stata-Version: 9
INSTALLATION FILES (click here to
install)
metaparm.ado
parmby.ado
parmcip.ado
parmest.ado
metaparm.hlp
metaparm_content_opts.hlp
metaparm_outdest_opts.hlp
metaparm_resultssets.hlp
parmby.hlp
parmby_only_opts.hlp
parmcip.hlp
parmcip_opts.hlp
parmest.hlp
parmest_ci_opts.hlp
parmest_outdest_opts.hlp
parmest_resultssets.hlp
parmest_varadd_opts.hlp
parmest_varmod_opts.hlp
------------------------------------------------------------------------
-------------------------
(click here to return to the previous screen)
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