plt.perm {CCP} | R Documentation |
Plot permutation distributions for test statistics
Description
This function plots permutation distributions for test statistics that are used to assign the statistical significance
of canonical correlation coefficients, see function p.perm
.
Usage
plt.perm(p.perm.out)
Arguments
p.perm.out |
output of |
Details
Depending on what type of statistic was chosen in p.perm
,
a permutation distribution of this statistic is shown.
The statistic is one of: Wilks' Lambda, Hotelling-Lawley Trace, Pillai-Bartlett Trace, or Roy's Largest Root.
These test statistics can be used to assign significance levels to canonical correlation coefficients,
for details see p.perm
.
The value corresponding to the "original" test statistic
(calculated using the canonical correlation coefficients resulting from unpermuted data )
is plotted as a red, dotted vertical line;
thus the area of the histogram outside this line determines the approximate p-value.
The vertical line is not visible if the value corresponding to the original test statistic
is in the far tail of the histogram, yielding a p-value which is (close to) zero.
The numerical value corresponding to the original test statistic is plotted in the subtitle of the graph,
as well as the calculated p-value.
The grey vertical line represents the mean of the permutation distribution.
Author(s)
Uwe Menzel <uwemenzel@gmail.com>
See Also
See the function p.perm
for the calculation of the p-values.
Examples
## Load the CCP package:
library(CCP)
## Simulate example data:
X <- matrix(rnorm(150), 50, 3)
Y <- matrix(rnorm(250), 50, 5)
## Calculate canonical correlations:
rho <- cancor(X,Y)$cor
## Define number of observations,
## and number of dependent and independent variables:
N = dim(X)[1]
p = dim(X)[2]
q = dim(Y)[2]
## Plot the permutation distribution of an F approximation
## for Wilks Lambda, considering 3 and 2 canonical correlations:
out1 <- p.perm(X, Y, nboot = 999, rhostart = 1)
plt.perm(out1)
out2 <- p.perm(X, Y, nboot = 999, rhostart = 2)
plt.perm(out2)
## Plot the permutation distribution of an F approximation
## for the Pillai-Bartlett Trace,
## considering 3, 2, and 1 canonical correlation(s):
res1 <- p.perm(X, Y, nboot = 999, rhostart = 1, type = "Pillai")
plt.perm(res1)
res2 <- p.perm(X, Y, nboot = 999, rhostart = 2, type = "Pillai")
plt.perm(res2)
res3 <- p.perm(X, Y, nboot = 999, rhostart = 3, type = "Pillai")
plt.perm(res3)