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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 scienti?c ?elds, to model observed data and as the basis for hypothesis tests. The use of such models requires speci?cation of a design matrix, and subsequent formulation of contrasts representing scienti?c hypotheses of interest. Proper execution of these steps requires a thorough understanding of the meaning of the individual coef?cients, 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 coef?cients are combined to give the ?tted values for each combination of predictor variables, which allows users to both extract the interpretation of each individual coef?cient, 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 in?ation factors and the pseudoinverse of the design matrix.

SUBMITTER: Soneson C

PROVIDER: S-EPMC7359746 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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

Soneson Charlotte C Marini Federico F Geier Florian F Love Michael I MI Stadler Michael B MB

F1000Research 20200604

Linear and generalized linear models are used extensively in many scientiﬁc ﬁelds, to model observed data and as the basis for hypothesis tests. The use of such models requires speciﬁcation of a design matrix, and subsequent formulation of contrasts representing scientiﬁc hypotheses of interest. Proper execution of these steps requires a thorough understanding of the meaning of the individual coefﬁcients, and is a frequent source of uncertainty for end users. Here, we present an R/Bioconductor p ...[more]

PMID: 32704355

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Dataset Information

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

Publications

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

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