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From |
Bob Fitzgerald <bfitzgerald@mprinc.com> |

To |
"Newson, Roger B" <r.newson@imperial.ac.uk> |

Subject |
Re: st: New versions of somersd and parmest on SSC |

Date |
Thu, 12 Jun 2008 09:05:22 -0700 |

Roger,

Thanks so much for this revision. I and my colleagues are indebted to you for your quick response and all the assistance you provided.

Best regards,

Bob

Newson, Roger B wrote:

Thanks to Kit Baum, new versions of the somersd and parmest packages (described as below on my website) are now available for download from SSC. In Stata, use the ssc command to do this. Both packages have been improved to increase computational efficiency. The improvements in the 2 packages are as follows: 1. The somersd package This is now updated to Stata 10, and has online help files suffixed .sthlp, documenting the results saved in e() and/or r() by each command, which, in the case of the somersd command, now include e(cmdline) (the command as typed). However, users of Stata 5, 6, 7, 8 and 9 can still download the Stata 5, 6 and 9 versions of somersd from my website. This is usually done by typing, in Stata, net from "http://www.imperial.ac.uk/nhli/r.newson/"; and downloading the package from the subdirectory for the Stata version required. Also, the censlope command now has a new iteration option nolimits, which specifies that the confidence limits for the percentile slopes will not be calculated. This is intended to save time for users who wish to calculate confidence limits for percentile slopes using resampling command prefixes, such as bootstrap:, jackknife:, or svy brr:. Usually, censlope calculates the estimates and confidence limits for percentile slopes iteratively, using 2 iteration sequences for the estimate and 1 iteration sequence for each of the confidence limits. The nolimits option therefore approximately halves the time taken to calculate bootstrap, jackknife or BRR confidence intervals for a Theil-Sen median slope. The bootstrap method is recommended for the Theil-Sen median slope by Wilcox (1998). I would like to thank Bob Fitzgerald of MPR Associates, Inc. for drawing my attention to some problems with subsampling methods and the somersd package, and Jeff Pitblado of StataCorp for his helpful advice on how they might be solved. 2. The parmest package All 4 modules of this package (parmest, parmby, parmcip and metaparm) perform the same functions as before. However, the code of all 4 modules has been updated internally. In the case of parmby, the internal code has been streamlined (using Mata), so that, if multiple by-groups are present, then parmby only inputs one by-group at a time (using an in-qualifier), instead of inputting them all and dropping all except the current one (using an if-qualifier). Predictably, this speeds up the execution if a very large number of by-groups are present. For instance, if the dataset is formed by concatenating 8192 copies of the auto dataset distributed with Stata, and the by-groups are the 16382 combinations of copy number and car type (US or foreign cars), implying 606208 observations, then, on my Windows system, the new version of parmby takes 1370.492 seconds to execute, but the old version of parmby takes 3841.310 seconds to execute (2.8028693 times as long). I would like to thank Mike Blasnik, David Elliott and David Airey for their very helpful discussion, research and advice on the computational issues involved in this streamlining process, and Vince Wiggins for warning me of the dangers of trying to do it another way (using the undocumented _prefix command suite). Best wishes Roger References Wilcox, R. R. 1998. A note on the Theil-Sen regression estimator when the regressor is random and the error term is heteroscedastic. Biometrical Journal 40: 261-268. Roger B 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: r.newson@imperial.ac.uk Web page: www.imperial.ac.uk/nhli/r.newson/ Departmental Web page: http://www1.imperial.ac.uk/medicine/about/divisions/nhli/respiration/pop genetics/reph/ Opinions expressed are those of the author, not of the institution. ------------------------------------------------------------------------ -------------------- package somersd from http://www.imperial.ac.uk/nhli/r.newson/stata10 ------------------------------------------------------------------------ -------------------- TITLE somersd: Kendall's tau-a, Somers' D and percentile slopes DESCRIPTION/AUTHOR(S) The somersd package contains the programs somersd, censlope and cendif, which calculate confidence intervals for a range of parameters behind rank or "nonparametric" statistics. somersd calculates confidence intervals for generalized Kendall's tau-a or Somers' D parameters, and stores the estimates and their covariance matrix as estimation results. It can be used on left-censored, right-censored, clustered and/or stratified data. censlope is an extended version of somersd, which also calculates confidence limits for the generalized Theil-Sen median slopes (or other percentile slopes) corresponding to the version of Somers' D or Kendall's tau-a estimated. cendif is an easy-to-use program to calculate confidence intervals for Hodges-Lehmann median differences (or other percentile differences) between two groups. The somersd package can be used to calculate confidence intervals for a wide range of rank-based parameters, which are special cases of Kendall's tau-a, Somers' D or percentile slopes. These parameters include differences between proportions, Harrell's c index, areas under receiver operating characteristic (ROC) curves, differences between Harrell's c indices or ROC areas, Gini coefficients, population attributable risks, median differences, ratios, slopes and per-unit ratios, and the parameters behind the sign test and the Wilcoxon-Mann-Whitney or Breslow-Gehan ranksum tests. Full documentation of the programs (including methods and formulas) can be found in the manual files somersd.pdf, censlope.pdf and cendif.pdf, which can be viewed using the Adobe Acrobat Reader. Author: Roger Newson Distribution-date: 06June2008 Stata-version: 10 INSTALLATION FILES (click here to install) cendif.ado censlope.ado somers_p.ado somersd.ado _bcsf_bisect.mata _bcsf_bracketing.mata _bcsf_regula.mata _bcsf_ridders.mata _blncdtree.mata _somdtransf.mata _u2jackpseud.mata _v2jackpseud.mata blncdtree.mata tidot.mata tidottree.mata lsomersd.mlib cendif.sthlp censlope.sthlp censlope_iteration.sthlp mf_bcsf_bracketing.sthlp mf_blncdtree.sthlp mf_somdtransf.sthlp mf_u2jackpseud.sthlp somersd.sthlp somersd_mata.sthlp ANCILLARY FILES (click here to get) cendif.pdf censlope.pdf somersd.pdf ------------------------------------------------------------------------ -------------------- (click here to return to the previous screen) ------------------------------------------------------------------------ -------------------- package parmest from http://www.imperial.ac.uk/nhli/r.newson/stata10 ------------------------------------------------------------------------ -------------------- 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: 10june2008 Stata-Version: 10 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 ------------------------------------------------------------------------ -------------------- (click here to return to the previous screen)

-- Robert Fitzgerald Senior Research Associate MPR Associates, Inc. 2150 Shattuck Ave. #800 Berkeley,CA 94702 (510) 849-4942 www.mprinc.com * * For searches and help try: * http://www.stata.com/support/faqs/res/findit.html * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**References**:**st: New versions of somersd and parmest on SSC***From:*"Newson, Roger B" <r.newson@imperial.ac.uk>

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