ExploreModelMatrix: Interactive exploration for improved understanding of design matrices and linear models in R

Abstract

Linear and generalized linear models are used extensively in many scienticelds, to model observed data and as the basis for hypothesis tests. The use of such models requires specication of a design matrix, and subsequent formulation of contrasts representing scientic hypotheses of interest. Proper execution of these steps requires a thorough understanding of the meaning of the individual coefcients, and is a frequent source of uncertainty for end users. Here, we present an R/Bioconductor package, ExploreModelMatrix, which enables interactive exploration of design matrices and linear model diagnostics. Given a sample annotation table and a desired design formula, the package displays how the model coefcients are combined to give the tted values for each combination of predictor variables, which allows users to both extract the interpretation of each individual coefcient, and formulate desired linear contrasts. In addition, the interactive interface displays informative characteristics for the regular linear model corresponding to the provided design, such as variance ination factors and the pseudoinverse of the design matrix.