`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.

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.

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.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 | ```
################################################################
## 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)
``` |

Questions? Problems? Suggestions? Tweet to @rdrrHQ or email at ian@mutexlabs.com.

Please suggest features or report bugs with the GitHub issue tracker.

All documentation is copyright its authors; we didn't write any of that.

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.