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.
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 multtest.gp
plot(x, signif = FALSE, alpha = 0.05,
vars = NULL, xlab = "Group", ylab = "Mean (+/- SE) value",
titles = NULL, groups = NULL, ...)
data frame containing response variables.
factor defining groups to compare.
type of test to use: parametric (default), permutational (non parametric) or rank-based (non parametric). See Details.
transformation to apply to response variables before testing (none by default; "4rt" is fourth-root). Only used for parametric and permutational tests.
value to add to response variables before a log-transformation.
method for p-values correction. See help of p.adjust.
logical indicating if variables should be ordered based on p-values.
object returned from multtest.gp.
logical indicating if only variables with significant P-value should be plotted.
significance threshold.
numeric vector giving variables to plot (rows of x). Default to all, which can lead to errors if too many variables.
legend of the x axis.
legend of the y axis
titles of the graphs (name of the variables by default).
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.
Maxime HERVE <maxime.herve@univ-rennes1.fr>
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.
perm.anova, perm.t.test
data(iris)
mult <- multtest.gp(iris[,1:4],iris$Species)
plot(mult)
Run the code above in your browser using DataLab