Monthly Archives: April 2009

Specialized Statistical Resources

Often researchers will need access to functionality that isn’t found in commercial statistics packages. This problem varies quite a bit and is meet with specialized solutions by the statistical community. These solutions are often cutting edge, reflecting new statistical research. Most stats packages allow some form of macro/code authorship. This works to a point and often provides a just in time solution. Well known examples include Matlab’s scripting language, SAS IML, GAUSS, Stata, Splus and R. Yet others will seek stand alone solutions in one form or another. These range from public domain C, C++, Fortran, and Java research subrountines to stand-alone programs with various user interfaces. The goal of this blog is to list references and short descriptions of various solutions that may offer additional insights into your research and the statistical methods, and maybe even save you some time. About a dozen or so topics some to mind and I hope to address them shortly. These posts are not intended as statistical guidance nor endorsement. Most problems are best addressed by the advice of an experienced practitioner in the relevant field.

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Statistical Power calculations

Statistical power calculations are often needed at various stages of planning for establishing sample sizes.
Elsewhere on this Blog I mention PiFace as a power calculation tool. However SAS users may find the following three SAS macros of interest.
UnifyPow is an extensive collection of power calculators implemented in SAS as a Macro. A SAS proceedings paper about UnifyPow discusses its broad generality. The second macro is rpower and addresses the reprospective aspect of the issue. The third macro, glimmixsamplesize, is designed to use the generality of SAS’s Proc Glimmix for generalized linear mixed models. These macros provide a substantial increase in the number of settings that can be addressed for power calculations.

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R graphics gallery

Sometimes it is important to reinvent the wheel, and sometimes not. Here is a site with a nice collection of contributed R graphic examples from a variety of R packages. Almost all are supportive of some statistical method for purposes of summary and presentation.

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