This is a stripped down version of the "cor.mtest()" function from the "corrplot" package. It uses
the stats::cor.test() function to calculate pairwise p-values. Unlike the corrplot version, this
only calculates p-values, and not confidence intervals. Credit to corrplot for this code, I only
replicate it here to avoid depending on their package for a single function.
Arguments
- X
A numeric matrix or data frame
- ...
Additional arguments passed to function
cor.test(), e.g.conf.level = 0.95.
Examples
# a matrix of random numbers, 3 cols
x <- matrix(runif(30), 10, 3)
# get correlations between cols
cor(x)
#> [,1] [,2] [,3]
#> [1,] 1.0000000 -0.4518163 -0.5778409
#> [2,] -0.4518163 1.0000000 0.4333702
#> [3,] -0.5778409 0.4333702 1.0000000
# get p values of correlations between cols
get_pvals(x)
#> [,1] [,2] [,3]
#> [1,] 0.00000000 0.1899003 0.08019865
#> [2,] 0.18990031 0.0000000 0.21087999
#> [3,] 0.08019865 0.2108800 0.00000000