Description Usage Arguments Details Value See Also Examples
Calculates the empirical probabilities of obtaining correlation coefficients equal or greater in squared magnitude than the ones observed, given the null hypothesis that the true correlations all equal 0.
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x |
a numeric matrix or data frame. |
y |
NULL (default) or a matrix or data frame with compatible dimensions to x. The default is equivalent to y = x (but more efficient). |
use |
an optional character string giving a method for computing covariances in the presence of missing values. This must be (an abbreviation of) one of the strings "everything", "all.obs", "complete.obs", "na.or.complete", or "pairwise.complete.obs" (default). |
method |
a character string indicating which correlation coefficient is to be computed. One of "pearson" (default), "kendall", or "spearman": can be abbreviated. |
n_perm |
The number of permutations. |
seed |
Integer used to seed the random number generator. |
n_cores |
Integer indicating number of processes to be used in parallel processing. Set to 1 fewer than the number of available CPUs by default. |
To calculate permutation-based P values for all bivariate relationships
between the columns of X and the columns of Y, the observations
in each column of X are randomly reshuffled (which simulates how the
data would be distributed if the null hypothesis were true), and all
bivariate correlations between the reshuffled X and Y are
recalculated, with the largest squared coefficient recorded. This procedure
is repeated n_perm
times to construct an empirical sampling
distribution of the largest correlation that one obtains by chance when the
null hypothesis is true and one computes the same number of correlations. The
correlations calculated from the real data are then squared and compared to
this distribution to determine an empirical adjusted P value. If n
permutations were carried out and r of the n largest squared
coefficients are equal to or greater than a given squared coefficient from
the actual correlation matrix, then the empirical adjusted P value for that
coefficient is given by (r + 1)/(n + 1), i.e., the frequency of values
in the null distribution that are at least as large as what was observed,
with an added constant of 1 to prevent a probability of 0 in the event that
the observed value lies entirely outside the null distribution.
Object of class "cor_perm", containing a cor_list
object with an additional vector of empirically adjusted p-values that
correct for multiple testing.
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