MultiFit (version 1.1.1)

MultiSummary: Summary of significant tests

Description

Provide a post-hoc summary of significant tests. See vignettes for further examples.

Usage

MultiSummary(xy, x = NULL, y = NULL, fit, alpha = 0.05,
  only.rk = NULL, use.pval = NULL, plot.tests = TRUE, pch = NULL,
  rd = 2, plot.margin = FALSE)

Arguments

xy

A list, whose first element corresponds to the matrix x as below, and its second element corresponds to the matrix y as below. if xy is not specified, x and y need to be assigned.

x

A matrix, number of columns = dimension of random vector, number of rows = number of observations.

y

A matrix, number of columns = dimension of random vector, number of rows = number of observations.

fit

An object generated by MultiFIT.

alpha

Numeric, only tests with adjusted p-values less than alpha are presented in the output.

only.rk

Positive integer vector. Show only tests that are ranked according to only.rk and have adjusted p-value below alpha. If left as NULL, all tests with adjusted p-values less than alpha are presented in the output.

use.pval

String, choose between "H" (for Holm), "Hcorrected" (for Holm on corrected p-values) or "MH" for modified Holm. If left NULL, the order of preference is "MH", "Hcorrected" and then "H", according to which is present in the object fit.

plot.tests

Logical, plot the marginal scatter plots that are associated with the presented significant tests.

pch

Point style for plots. If left as NULL, a default combination of crosses and bullets is applied.

rd

Numeric, number of figures to round to when presenting ranges of variables.

plot.margin

Logical, plot the marginal scatter plot of the margins that are associated with each significant test, without highlighting which points are conditioned on and are in the discretized 2x2 contingency table.

Value

List whose elements are significant.tests, a data frame that summarizes the main features of the tests and their overall ranking by p-value and original.scale.cuboids, a list whose number of elements is equal to the number of significant tests (the same number of rows of the data frame significant.tests). Each element corresponds to a test and is a list whose elements are the marginal ranges of the associated cuboid.

Examples

# NOT RUN {
set.seed(1)
n = 300
Dx = Dy = 2
x = matrix(0, nrow = n, ncol = Dx)
y = matrix(0, nrow = n, ncol = Dy)
x[,1] = rnorm(n)
x[,2] = runif(n)
y[,1] = rnorm(n)
y[,2] = sin(5 * pi * x[ , 2]) + 1 / 5 * rnorm(n)
fit = MultiFIT(x = x, y = y, verbose = TRUE)
w = MultiSummary(x = x, y = y, fit = fit, alpha = 0.0001)
# }