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Matrix analysis for statistics James R. Schott
Publisher: Wiley-Interscience

I just gave a journal club presentation on this wonderful piece of software. Power analysis is a very useful tool to estimate the statistical power from a study. Goriely show that, in estimating the number of eigenvectors which are of statistical significance in the PCA analysis of DNA microarray data, the method of cumulative percent of variance should not be used. Power Analysis and the Probability of Errors. It was very well received and I really hope more people use this. Groupmeans = as.matrix(by(x$value,x$group,mean));. The modification incorporates a test for the presence of an "effective degeneracy" among the subspaces spanned by the eigenvectors of the correlation matrix of the data set then allocates the total variance among subspaces. Matrix Analysis of the Digital Divide in eHealth Services Using Awareness, Want, and Adoption Gap. A summary of the For example, R. Matrix eQTL: ultra fast eQTL analysis. Analyses include summary statistics, crosstabs, linear regression, logistic regression, covariance matrix computations for factor analysis and principal components, and k-means clustering.