English, Article, Journal or magazine article edition: Creating plots and tables of estimation results using parmest and friends Roger Newson

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Bookmark: http://trove.nla.gov.au/version/176158
Physical Description
  • preprint
Language
  • English

Edition details

Title
  • Creating plots and tables of estimation results using parmest and friends
Author
  • Roger Newson
Physical Description
  • preprint
Notes
  • Statisticians make their living mostly by producing confidence intervals and p-values. However, those supplied in the Stata log are not in any fit state to be delivered to the end user, who usually at least wants them tabulated and formatted, and may appreciate them even more if they are plotted on a graph for immediate impact. The parmest package was developed to make this easy, and consists of two programs. These are parmest, which converts the latest estimation results to a data set with one observation per estimated parameter and data on confidence intervals, p-values and other estimation results, and parmby, a ``quasi-byable'' front end to parmest, which is like statsby, but creates a data set with one observation per parameter per by-group instead of a data set with one observation per by-group. The parmest package can be used together with a team of other Stata programs to produce a wide range of tables and plots of confidence intervals and p-values. The programs descsave and factext can be used with parmby to create plots of confidence intervals against values of a categorical factor included in the fitted model, using dummy variables produced by xi or tabulate. The user may easily fit multiple models, produce a parmby output data set for each one, and concatenate these output data sets using the program dsconcat to produce a combined data set, which can then be used to produce tables or plots involving parameters from all the models. For instance, the user might tabulate or plot unadjusted and adjusted regression parameters side by side, together with their confidence limits and/​or p-values. The parmest team is particularly useful when dealing with large volumes of results derived from multiple multi-parameter models, which are particularly common in the world of epidemiology.
  • RePEc:boc:usug02:1
Language
  • English
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