MVLM-package: Multivariate Linear Model with Analytic p-values

In MVLM: Multivariate Linear Model with Analytic p-Values

Description

The MVLM package is used to fit linear models with a multivariate outcome. It utilizes the asymptotic null distribution of the multivariate linear model test statistic to compute p-values (McArtor et al., under review). It therefore alleviates the need to use approximate p-values based Wilks Lambda, Pillai's Trace, the Hotelling-Lawley Trace, and Roy's Greatest Root.

Usage

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

vignette("mvlm-vignette")

There is one primary function that comprises this package: vignette('mvlm-vignette') There is one primary functions that comprise this package: mvlm, which regresses a multivariate outcome onto a set of predictors. Standard functions like summary, fitted, residuals, and predict can be called on a mvlm output object.

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., Grasman, R. P. P. P., Lubke, G. H., & Bergeman, C. S. (under review). The asymptotic null distribution of the multivariate linear model test statistic. Manuscript submitted for publication.

Examples

data(mvlmdata)
Y <- as.matrix(Y.mvlm)
mvlm.res <- mvlm(Y ~ Cont + Cat + Ord, data = X.mvlm)
summary(mvlm.res)

MVLM documentation built on May 2, 2019, 8:47 a.m.