Description Usage Arguments Value
This function performs a Canonical Correlation Analysis on a correlation matrix. It assumes that the correlation matrix contains variable column names. If no 'nObs' is supplied, no significance test will be performed.
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corMat |
A symmetric correlation matrix. |
p |
The number of variables in the first set. Used to separate the sets. |
set1name |
Name of the first set. Defaults to "set1". |
set2name |
Name of the second set. Defaults to "set2". |
useColumnNames |
Boolean that indicates whether to use the column names in the supplied correlation matrix. Defaults to True. |
set1VarPrefix |
Prefix used for the variables in set 1. Ignored if useColumnNames=True and if all columns in the correlation matrix contain variable names. Defaults to "U". |
set2VarPrefix |
Prefix used for the variables in set 2. Ignored if useColumnNames=True and if all columns in the correlation matrix contain variable names. Defaults to "V". |
nObs |
Number of observations. Used for testing significance of the canonical variate pairs. |
alpha |
Significance level at which the test will be evaluated. |
A list object with CCA information.
corMat - The correlation matrix used for the analysis (unchanged).
RCC - A list containing the raw correlation coefficients for both sets.
SCC - A vector containing the sample canonical correlations between the variate pairs.
R2 - A vector containing the squared sample canonical correlations between the variate pairs.
prExVar - A dataframe containing the proportions of explained variance of the variate pairs.
testRes - The result of the large sample test.
additionalCorrelations - A list containing additional correlation matrices.
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