plt.perm | R Documentation |
This function plots permutation distributions for test statistics that are used to assign the statistical significance
of canonical correlation coefficients, see function p.perm
.
plt.perm(p.perm.out)
p.perm.out |
output of |
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.
Uwe Menzel <uwemenzel@gmail.com>
See the function p.perm
for the calculation of the p-values.
## 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)
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