Multivariate Distance Matrix Regression

Description

MDMR allows a user to conduct multivariate distance matrix regression using analytic p-values and measures of effect size described by McArtor et al. (second revision under review). Analytic p-values are computed using the R package CompQuadForm (Duchesne & De Micheaux, 2010).

Usage

To access this package's tutorial, type the following line into the console:

vignette('mdmr-vignette')

There are two primary functions that comprise this package: mdmr, which regresses a distance matrix onto a set of predictors, and delta, which computes measures of univariate effect size in the context of multivariate distance matrix regression. The help files of both functions provide more general information than the package vignette.

References

Davies, R. B. (1980). The Distribution of a Linear Combination of chi-square Random Variables. Journal of the Royal Statistical Society. Series C (Applied Statistics), 29(3), 323-333.

Duchesne, P., & De Micheaux, P.L. (2010). Computing the distribution of quadratic forms: Further comparisons between the Liu-Tang-Zhang approximation and exact methods. Computational Statistics and Data Analysis, 54(4), 858-862.

McArtor, D. B., Lubke, G. H., & Bergeman, C. S. (second revision under review). Extending multivariate distance matrix regression with an effect size measure and the distribution of the test statistic.

Examples

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data(mdmrdata)
D <- dist(Y.mdmr, method = 'euclidean')

mdmr.res <- mdmr(X = X.mdmr, D = D)
summary(mdmr.res)

mdmr.delta <- delta(X = X.mdmr, Y = Y.mdmr, dtype = 'euclidean',
                   niter = 1, seed = 12345)