Description Usage Arguments Value Examples
Provide a posthoc summary of significant tests. See vignettes for further examples.
1 2 3 
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 
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 
alpha 
Numeric, only tests with adjusted 
only.rk 
Positive integer vector. Show only tests that are ranked according to

use.pval 
String, choose between 
plot.tests 
Logical, plot the marginal scatter plots that are associated with the presented significant tests. 
pch 
Point style for plots. If left as 
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. 
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.
1 2 3 4 5 6 7 8 9 10 11  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)

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