lpcor: Linear and Partial Correlation Coefficients In metan: Multi Environment Trials Analysis

Description

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

Usage

 1 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

 1 2 3 4 5 6 7 8 9 10 11 12 13 14 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)

metan documentation built on Nov. 10, 2021, 9:11 a.m.