mcroast: Multiple Contrast Rotation Tests
In daniel-gerhard/mcroast: Multiple Contrast Rotation Tests

Description Usage Arguments Details Value Author(s) References Examples

Adaption of the roast function in package limma to perform a maxT-test in the multivariate linear model, controlling FWER for multiple contrasts.

1	mcroast(formula, data = NULL, K, nrot = 9999, adjusted = TRUE, moderated = FALSE)

`formula`	A formula object, analogously to lm, with a matrix of response variables (independent subjects in rows) on the left, and design covariates on the right hand side.
`data`	A data.frame object, searching for column names in formula.
`K`	A contrast matrix (different contrasts in rows), with the same number of columns as number of parameters in the model
`nrot`	Number of Monte Carlo simulation steps
`adjusted`	logical, if TRUE single-step maxT FWER control is performed; if FALSE the comparison-wise error rate is controlled
`moderated`	logical, if TRUE, moderated statistics (Smyth 2004) are used.

Most of the code was taken from function roast in package limma (Bioconductor).

A list with

`Y`	The matrix of response variables
`X`	The design matrix
`statistic`	A matrix of test statistics
`pvalue`	A matrix of (adjusted) p-values
`B`	A matrix with contrast parameter estimates
`SD`	A matrix with estimated standard deviations
`mrot`	A vector with simulated maxT statistics under H0

Daniel Gerhard

Wu, D, Lim, E, Francois Vaillant, F, Asselin-Labat, M-L, Visvader, JE, and Smyth, GK (2010). ROAST: rotation gene set tests for complex microarray experiments. Bioinformatics 26, 2176-2182.

Langsrud, O (2005). Rotation tests. Statistics and Computing 15, 53-60.

### data example from Hasler & Hothorn (2011), 
### A Dunnett-Type Procedure for Multiple Endpoints,
### The International Journal of Biostatistics: Vol. 7: Iss. 1, Article 3.
### DOI: 10.2202/1557-4679.1258
# but using two-sided inference

### see ?coagulation
data("coagulation", package = "SimComp")

### Dunnett contrast for comparisons to a control
K <- multcomp::contrMat(table(coagulation$Group), type="Dunnett")

### Rotation test with FWER control
(mcr <- mcroast(cbind(Thromb.count, ADP, TRAP) ~ Group-1, data=coagulation, K=K))
mcr$pvalue

### Simultaneous confidence intervals
confint(mcr)