MDMR-package: Multivariate Distance Matrix Regression

Description Usage References Examples

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. (2017). Analytic p-values are computed using the R package CompQuadForm (Duchesne & De Micheaux, 2010). It also facilitates the use of MDMR on samples consisting of (hierarchically) clustered observations.

Usage

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

vignette('mdmr-vignette')

There are three 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 third function mixed.mdmr facilitates the use of MDMR on (hierarchically) clustered samples using an approach analogous to the linearar mixed-effects model for univariate outcomes. The help files of all all three 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. (2017). Extending multivariate distance matrix regression with an effect size measure and the distribution of the test statistic. Psychometrika. Advance online publication.

McArtor, D. B. (2017). Extending a distance-based approach to multivariate multiple regression (Doctoral Dissertation).

McArtor, D. B. & Lubke, G. H. (in preparation). Multivariate distance matrix regression with hierarchically clustered samples.

Examples

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################################################################
## Conducting MDMR on data comprised of independent observations
################################################################
# Source data
data(mdmrdata)

# Get distance matrix
D <- dist(Y.mdmr, method = 'euclidean')

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

################################################################
## Conducting MDMR on data comprised of dependent observations
################################################################
# Source data
data("clustmdmrdata")

# Get distance matrix
D <- dist(Y.clust)

# Conduct mixed-MDMR
mixed.res <- mixed.mdmr(~ x1 + x2 + (x1 + x2 | grp),
                        data = X.clust, D = D)
summary(mixed.res)

MDMR documentation built on May 29, 2017, 5:30 p.m.

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