multtest.gp: Univariate comparison of groups for multiple variables

In RVAideMemoire: Testing and Plotting Procedures for Biostatistics

Univariate comparison of groups for multiple variables

Description

Performs group comparisons for multiple variables using parametric, permutational or rank tests, and corrects p-values. Gives also group means and standards errors for each variable.

Usage

multtest.gp(tab, fac, test = c("param", "perm", "rank"),
  transform = c("none", "sqrt", "4rt", "log"), add = 0, p.method = "fdr",
  ordered = TRUE, ...)

## S3 method for class 'multtest.gp'
plot(x, signif = FALSE, alpha = 0.05,
  vars = NULL, xlab = "Group", ylab = "Mean (+/- SE) value",
  titles = NULL, groups = NULL, ...)

Arguments

tab	data frame containing response variables.
fac	factor defining groups to compare.
test	type of test to use: parametric (default), permutational (non parametric) or rank-based (non parametric). See Details.
transform	transformation to apply to response variables before testing (none by default; "4rt" is fourth-root). Only used for parametric and permutational tests.
add	value to add to response variables before a log-transformation.
p.method	method for p-values correction. See help of p.adjust.
ordered	logical indicating if variables should be ordered based on p-values.
x	object returned from multtest.gp.
signif	logical indicating if only variables with significant P-value should be plotted.
alpha	significance threshold.
vars	numeric vector giving variables to plot (rows of x). Default to all, which can lead to errors if too many variables.
xlab	legend of the x axis.
ylab	legend of the y axis
titles	titles of the graphs (name of the variables by default).
groups	names of the bars (levels of fac by default).
...	additional arguments to testing functions in multtest.gp (especially for var.equal in t.test and nperm in perm.anova and perm.t.test) and to barplot in plot.

Details

In case of parametric tests, t-tests or ANOVAs are used depending on the number of groups (2 or more, respectively). In case of permutational tests: permutational t-tests or permutational ANOVAs. In case of rank-based tests: Mann-Whitney-Wilcoxon or Kruskal-Wallis tests.

Author(s)

Maxime HERVE <[e-mail]>

See Also

perm.anova, perm.t.test

Examples

data(iris)
mult <- multtest.gp(iris[,1:4],iris$Species)
plot(mult)

RVAideMemoire documentation built on Nov. 6, 2023, 5:07 p.m.