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)
# }