lpcor: Linear and Partial Correlation Coefficients
In TiagoOlivoto/WAASB: Multi Environment Trials Analysis

lpcor

R Documentation

Linear and Partial Correlation Coefficients

Description

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

Usage

lpcor(.data, ..., by = NULL, n = NULL, method = "pearson")

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(s)

Tiago Olivoto tiagoolivoto@gmail.com

Examples


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)

TiagoOlivoto/WAASB documentation built on March 20, 2024, 4:18 p.m.