Computes Pearson's linear correlation or partial correlation with p-values

## Arguments

- data
The data set. It understand grouped data passed from

`dplyr::group_by()`

.- ...
Variables to use in the correlation. If no variable is informed all the numeric variables from

`data`

are used.- type
The type of correlation to be computed. Defaults to

`"linear"`

. Use`type = "partial"`

to compute partial correlation.- method
a character string indicating which partial correlation coefficient is to be computed. One of "pearson" (default), "kendall", or "spearman"

- use
an optional character string giving a method for computing covariances in the presence of missing values. See stats::cor for more details

- by
One variable (factor) to compute the function by. It is a shortcut to

`dplyr::group_by()`

.This is especially useful, for example, to compute correlation matrices by levels of a factor.- verbose
Logical argument. If

`verbose = FALSE`

the code is run silently.

## Details

The partial correlation coefficient is a technique based on matrix operations that allow us to identify the association between two variables by removing the effects of the other set of variables present (Anderson 2003) A generalized way to estimate the partial correlation coefficient between two variables (i and j ) is through the simple correlation matrix that involves these two variables and m other variables from which we want to remove the effects. The estimate of the partial correlation coefficient between i and j excluding the effect of m other variables is given by: \[r_{ij.m} = \frac{{- {a_{ij}}}}{{\sqrt {{a_{ii}}{a_{jj}}}}}\]

Where \(r_{ij.m}\) is the partial correlation coefficient between variables i and j, without the effect of the other m variables; \(a_{ij}\) is the ij-order element of the inverse of the linear correlation matrix; \(a_{ii}\), and \(a_{jj}\) are the elements of orders ii and jj, respectively, of the inverse of the simple correlation matrix.

## References

Anderson, T. W. 2003. An introduction to multivariate statistical analysis. 3rd ed. Wiley-Interscience.

## Author

Tiago Olivoto tiagoolivoto@gmail.com

## Examples

```
# \donttest{
library(metan)
# All numeric variables
all <- corr_coef(data_ge2)
# Select variable
sel <-
corr_coef(data_ge2,
EP, EL, CD, CL)
sel$cor
#> EP EL CD CL
#> EP 1.0000000 0.2634237 0.1750448 0.3908239
#> EL 0.2634237 1.0000000 0.9118653 0.2554068
#> CD 0.1750448 0.9118653 1.0000000 0.3003636
#> CL 0.3908239 0.2554068 0.3003636 1.0000000
# Select variables, partial correlation
sel <-
corr_coef(data_ge2,
EP, EL, CD, CL,
type = "partial")
sel$cor
#> EP EL CD CL
#> EP 1.0000000 0.2938850 -0.2418441 0.3856626
#> EL 0.2938850 1.0000000 0.9110035 -0.1549749
#> CD -0.2418441 0.9110035 1.0000000 0.2454591
#> CL 0.3856626 -0.1549749 0.2454591 1.0000000
# }
```