tcR (version 2.2.4)

permutDistTest: Monte Carlo permutation test for pairwise and one-vs-all-wise within- and inter-group differences in a set of repertoires.

Description

WARNING: this is an experimental procedure, work is still in progress.

Perform permutation tests of distances among groups for the given groups of samples and matrix of distances among all samples.

Usage

permutDistTest(.mat, .groups, .n = 1000, .fun = mean, .signif = 0.05,
  .plot = T, .xlab = "Values",
  .title = "Monte Carlo permutation testing of overlaps",
  .hjust = -0.1, .vjust = -4)

Arguments

.mat

Symmetric matrix of repertoire distances.

.groups

Named list with names of repertoires in groups.

.n

Number of permutations for each pair of group.

.fun

A function to apply to distances.

.signif

Significance level. Below this value hypotheses counts as significant.

.plot

If T than plot the output results. Else return them as a data frame.

.xlab

X lab label.

.title

Main title of the plot.

.hjust

Value for adjusting the x coordinate of p-value labels on plots.

.vjust

Value for adjusting the y coordinate of p-value labels on plots.

See Also

repOverlap, intersectClonesets, ozScore, pca2euclid

Examples

# NOT RUN {
data(twb)
mat <- repOverlap(twb)
permutDistTest(mat, list(tw1 = c('Subj.A', 'Subj.B'), tw2 = c('Subj.C', 'Subj.D')))
permutDistTest(mat, list(tw1 = c('Subj.A', 'Subj.B'), tw2 = c('Subj.C', 'Subj.D')), .fun = median)
# }