Estimates the linear and partial correlation coefficients using as input a data frame or a correlation matrix.
lpcor(.data, ..., by = NULL, n = NULL, method = "pearson")
.data | The data to be analyzed. It must be a symmetric correlation
matrix or a data frame, possible with grouped data passed from
|
---|---|
... | Variables to use in the correlation. If |
by | One variable (factor) to compute the function by. It is a shortcut
to |
n | If a correlation matrix is provided, then |
method | a character string indicating which correlation coefficient is to be computed. One of 'pearson' (default), 'kendall', or 'spearman'. |
If .data
is a grouped data passed from
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.
Tiago Olivoto tiagoolivoto@gmail.com
# \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) # }