multiSummary {MultiFit} | R Documentation |
Provide a post-hoc summary of significant tests. See vignettes for further examples.
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)
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.
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)