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 across 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 rcorr
tidy(x, diagonal = FALSE, ...)
An rcorr
object returned from Hmisc::rcorr()
.
Logical indicating whether or not to include diagonal
elements of the correlation matrix, or the correlation of a column with
itself. For the elements, estimate
is always 1 and p.value
is always
NA
. Defaults to FALSE
.
Additional arguments. Not used. Needed to match generic
signature only. Cautionary note: Misspelled arguments will be
absorbed in ...
, where they will be ignored. If the misspelled
argument has a default value, the default value will be used.
For example, if you pass conf.lvel = 0.9
, all computation will
proceed using conf.level = 0.95
. Additionally, if you pass
newdata = my_tibble
to an augment()
method that does not
accept a newdata
argument, it will use the default value for
the data
argument.
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 columns:
Name or index of the first column being described.
Name or index of the second column being described.
The estimated value of the regression term.
The two-sided p-value associated with the observed statistic.
Number of observations used to compute the correlation
# feel free to ignore the following line—it allows {broom} to supply
# examples without requiring the model-supplying package to be installed.
if (requireNamespace("Hmisc", quietly = TRUE)) {
# load libraries for models and data
library(Hmisc)
mat <- replicate(52, rnorm(100))
# add some NAs
mat[sample(length(mat), 2000)] <- NA
# also, column names
colnames(mat) <- c(LETTERS, letters)
# fit model
rc <- rcorr(mat)
# summarize model fit with tidiers + visualization
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()
}
#> Loading required package: Formula
#>
#> Attaching package: ‘Hmisc’
#> The following object is masked from ‘package:psych’:
#>
#> describe
#> The following object is masked from ‘package:network’:
#>
#> is.discrete
#> The following object is masked from ‘package:survey’:
#>
#> deff
#> The following object is masked from ‘package:quantreg’:
#>
#> latex
#> The following objects are masked from ‘package:dplyr’:
#>
#> src, summarize
#> The following objects are masked from ‘package:base’:
#>
#> format.pval, units