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st: New version of -dsconcat- on SSC

From   Roger Newson <>
Subject   st: New version of -dsconcat- on SSC
Date   Wed, 30 Oct 2002 12:46:23 +0000

Dear All

Thanks to Kit Baum, there is now a new version of my -dsconcat- package available on SSC. To find how to install it, type -findit dsconcat-, -ssc desc dsconcat- or -net search dsconcat- inside Stata.

-dsconcat- is a multi-file version of -use-. It concatenates a list of Stata data files into a new data set in the memory, overwriting any existing data. Optionally, -dsconcat- creates new variables indicating, for each observation in the new data set, the data set of origin of that observation and the sequential order of that observation in its data set of origin. The new version has an added -subset- option, allowing the user to select subsets of the observations and/or the variables in the input data sets. The -subset- option is specified by the user as a combination of a -varlist- and/or an -if- qualifier and/or an -in- qualifier. For instance, the user might type

dsconcat auto1 auto2 auto3 auto4, subset(make foreign mpg weight in 1/53) dsn(dslab)

and create, in memory, a data set containing the variables -make-, -foreign-, -mpg- and -weight- in observations 1-53 of each of the data sets -auto1-, -auto2-, -auto3- and -auto4-, plus a new string variable -dslab-, storing the name of the input data set for each observation. This is useful if you want to concatenate a small subset of each of a large number of large data sets without using up too much memory. (I often want to do this when I have created a large number of output data sets containing regression parameters for a large number of large logistic regression models using the -parmest- package (also on SSC), and want to create a new data set containing only the interesting odds ratios corresponding to the interesting exposures and excluding the uninteresting odds ratios corresponding to confounders. I can then use the new data set of interesting odds ratios as input to the -smileplot- package to do multiple test procedures, or even to produce histograms of the z-scores to demonstrate to my colleagues a beautiful fit to the normal(0,1) distribution expected if all null hypotheses are true.)

Best wishes


Roger Newson
Lecturer in Medical Statistics
Department of Public Health Sciences
King's College London
5th Floor, Capital House
42 Weston Street
London SE1 3QD
United Kingdom

Tel: 020 7848 6648 International +44 20 7848 6648
Fax: 020 7848 6620 International +44 20 7848 6620
or 020 7848 6605 International +44 20 7848 6605

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

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