Abstract
The POWERLIBSAS/IML software provides convenient power calculations for a wide range of multivariate linear models with Gaussian errors. The software includes the Box, Geisser-Greenhouse, Huynh-Feldt, and uncorrected tests in the "univariate" approach to repeated measures (UNIREP), the Hotelling Lawley Trace, Pillai-Bartlett Trace, and Wilks Lambda tests in "multivariate" approach (MULTIREP), as well as a limited but useful range of mixed models. The familiar univariate linear model with Gaussian errors is an important special case. For estimated covariance, the software provides confidence limits for the resulting estimated power. All power and confidence limits values can be output to a SAS dataset, which can be used to easily produce plots and tables for manuscripts.
Highlights
The POWERLIB SAS/IML software provides convenient power calculations for a wide range of multivariate linear models with Gaussian errors
POWERLIB is a suite of SAS/IML modules which computes statistical power for hypothesis tests in a wide variety of univariate, multivariate, and repeated measures linear models with Gaussian errors and fixed predictors
The univariate” approach to repeated measures (UNIREP) approach can be shown to be equivalent to a restricted class of linear mixed models with Gaussian errors which meet the following restrictions (Gurka et al 2007): 1. No missing or mistimed observations
Summary
Available models and hypothesis tests POWERLIB computes power for the four commonly used tests for the “univariate” approach to repeated measures: Box, Geisser-Greenhouse, Huynh-Feldt, and uncorrected. These four tests and associated methods will be collectively referred to as the UNIREP approach. The program computes power for the three most popular multivariate test statistics: Hotelling Lawley Trace, Pillai-Bartlett Trace, and Wilks Lambda. In this manual, these three tests and related methods are collectively referred to as the MULTIREP approach. The UNIREP approach can be shown to be equivalent to a restricted class of linear mixed models with Gaussian errors which meet the following restrictions (Gurka et al 2007): 1. No missing or mistimed observations (all subjects have the same number of observations at the same within-subject levels)
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