tidy.rcorr {broom} | R Documentation |
Tidy summarizes information about the components of a model. A model component might be a single term in a regression, a single hypothesis, a cluster, or a class. Exactly what tidy considers to be a model component varies cross models but is usually self-evident. If a model has several distinct types of components, you will need to specify which components to return.
## S3 method for class 'rcorr' tidy(x, diagonal = FALSE, ...)
x |
An |
diagonal |
Logical indicating whether or not to include diagonal
elements of the correlation matrix, or the correlation of a column with
itself. For the elements, |
... |
Additional arguments. Not used. Needed to match generic
signature only. Cautionary note: Misspelled arguments will be
absorbed in |
Suppose the original data has columns A and B. In the correlation
matrix from rcorr
there may be entries for both the cor(A, B)
and
cor(B, A)
. Only one of these pairs will ever be present in the tidy
output.
A tibble::tibble with one row for each unique pair of columns in the correlatin matrix and columns:
column1 |
Name or index of the first column being described |
column2 |
Name or index of the second column being described |
estimate |
Estimate of Pearson's r or Spearman's rho |
n |
Number of observations used to compute the correlation |
p.value |
P-value of correlation |
if (requireNamespace("Hmisc", quietly = TRUE)) { library(Hmisc) mat <- replicate(52, rnorm(100)) # add some NAs mat[sample(length(mat), 2000)] <- NA # also column names colnames(mat) <- c(LETTERS, letters) rc <- rcorr(mat) td <- tidy(rc) td library(ggplot2) ggplot(td, aes(p.value)) + geom_histogram(binwidth = .1) ggplot(td, aes(estimate, p.value)) + geom_point() + scale_y_log10() }