WGCNA (version 1.70-3)

bicorAndPvalue: Calculation of biweight midcorrelations and associated p-values

Description

A faster, one-step calculation of Student correlation p-values for multiple biweight midcorrelations, properly taking into account the actual number of observations.

Usage

bicorAndPvalue(x, y = NULL, 
             use = "pairwise.complete.obs", 
             alternative = c("two.sided", "less", "greater"),
             ...)

Arguments

x

a vector or a matrix

y

a vector or a matrix. If NULL, the correlation of columns of x will be calculated.

use

determines handling of missing data. See bicor for details.

alternative

specifies the alternative hypothesis and must be (a unique abbreviation of) one of "two.sided", "greater" or "less". the initial letter. "greater" corresponds to positive association, "less" to negative association.

…

other arguments to the function bicor.

Value

A list with the following components, each a marix:

bicor

the calculated correlations

p

the Student p-values corresponding to the calculated correlations

Z

Fisher transform of the calculated correlations

t

Student t statistics of the calculated correlations

nObs

Numbers of observations for the correlation, p-values etc.

Details

The function calculates the biweight midcorrelations of a matrix or of two matrices and the corresponding Student p-values. The output is not as full-featured as cor.test, but can work with matrices as input.

References

Peter Langfelder, Steve Horvath (2012) Fast R Functions for Robust Correlations and Hierarchical Clustering. Journal of Statistical Software, 46(11), 1-17. https://www.jstatsoft.org/v46/i11/

See Also

bicor for calculation of correlations only;

cor.test for another function for significance test of correlations

Examples

# NOT RUN {
# generate random data with non-zero correlation
set.seed(1);
a = rnorm(100);
b = rnorm(100) + a;
x = cbind(a, b);
# Call the function and display all results
bicorAndPvalue(x)
# Set some components to NA
x[c(1:4), 1] = NA
corAndPvalue(x)
# Note that changed number of observations.
# }