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.

## Usage

```
# S3 method for rcorr
tidy(x, diagonal = FALSE, ...)
```

## Arguments

- x
An

`rcorr`

object returned from`Hmisc::rcorr()`

.- 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,

`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`

. Two exceptions here are:

## Details

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.

## Value

A `tibble::tibble()`

with columns:

- column1
Name or index of the first column being described.

- column2
Name or index of the second column being described.

- estimate
The estimated value of the regression term.

- p.value
The two-sided p-value associated with the observed statistic.

- n
Number of observations used to compute the correlation

## Examples

```
# load libraries for models and data
library(Hmisc)
#>
#> 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
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
#> # A tibble: 1,326 × 5
#> column1 column2 estimate n p.value
#> <chr> <chr> <dbl> <int> <dbl>
#> 1 B A -0.0806 41 0.616
#> 2 C A -0.194 38 0.242
#> 3 C B 0.0811 37 0.633
#> 4 D A -0.451 37 0.00505
#> 5 D B -0.258 35 0.134
#> 6 D C -0.183 35 0.292
#> 7 E A -0.0593 42 0.709
#> 8 E B 0.0208 45 0.892
#> 9 E C -0.228 44 0.136
#> 10 E D -0.0134 34 0.940
#> # ℹ 1,316 more rows
library(ggplot2)
ggplot(td, aes(p.value)) +
geom_histogram(binwidth = .1)
ggplot(td, aes(estimate, p.value)) +
geom_point() +
scale_y_log10()
```