Estimates the linear and partial correlation coefficients using as input a data frame or a correlation matrix.

## Arguments

- .data
The data to be analyzed. It must be a symmetric correlation matrix or a data frame, possible with grouped data passed from

`dplyr::group_by()`

.- ...
Variables to use in the correlation. If

`...`

is null (Default) then all the numeric variables from`.data`

are used. It must be a single variable name or a comma-separated list of unquoted variables names.- by
One variable (factor) to compute the function by. It is a shortcut to

`dplyr::group_by()`

. To compute the statistics by more than one grouping variable use that function.- n
If a correlation matrix is provided, then

`n`

is the number of objects used to compute the correlation coefficients.- method
a character string indicating which correlation coefficient is to be computed. One of 'pearson' (default), 'kendall', or 'spearman'.

## Value

If `.data`

is a grouped data passed from
`dplyr::group_by()`

then the results will be returned into a
list-column of data frames, containing:

**linear.mat**The matrix of linear correlation.**partial.mat**The matrix of partial correlations.**results**Hypothesis testing for each pairwise comparison.

## Author

Tiago Olivoto tiagoolivoto@gmail.com

## Examples

```
# \donttest{
library(metan)
partial1 <- lpcor(iris)
# Alternatively using the pipe operator %>%
partial2 <- iris %>% lpcor()
# Using a correlation matrix
partial3 <- cor(iris[1:4]) %>%
lpcor(n = nrow(iris))
# Select all numeric variables and compute the partial correlation
# For each level of Species
partial4 <- lpcor(iris, by = Species)
# }
```