metan (version 1.15.0)

can_corr: Canonical correlation analysis

Description

Performs canonical correlation analysis with collinearity diagnostic, estimation of canonical loads, canonical scores, and hypothesis testing for correlation pairs.

Usage

can_corr(
  .data,
  FG,
  SG,
  by = NULL,
  use = "cor",
  test = "Bartlett",
  prob = 0.05,
  center = TRUE,
  stdscores = FALSE,
  verbose = TRUE,
  collinearity = TRUE
)

Arguments

.data

The data to be analyzed. It can be a data frame (possible with grouped data passed from dplyr::group_by().

FG, SG

A comma-separated list of unquoted variable names that will compose the first (smallest) and second (highest) group of the correlation analysis, respectively. Select helpers are also allowed.

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.

use

The matrix to be used. Must be one of 'cor' for analysis using the correlation matrix (default) or 'cov' for analysis using the covariance matrix.

test

The test of significance of the relationship between the FG and SG. Must be one of the 'Bartlett' (default) or 'Rao'.

prob

The probability of error assumed. Set to 0.05.

center

Should the data be centered to compute the scores?

stdscores

Rescale scores to produce scores of unit variance?

verbose

Logical argument. If TRUE (default) then the results are shown in the console.

collinearity

Logical argument. If TRUE (default) then a collinearity diagnostic is performed for each group of variables according to Olivoto et al.(2017).

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.

• Matrix The correlation (or covariance) matrix of the variables

• MFG, MSG The correlation (or covariance) matrix for the variables of the first group or second group, respectively.

• MFG_SG The correlation (or covariance) matrix for the variables of the first group with the second group.

• Coef_FG, Coef_SG Matrix of the canonical coefficients of the first group or second group, respectively.

• Loads_FG, Loads_SG Matrix of the canonical loadings of the first group or second group, respectively.

• Score_FG, Score_SG Canonical scores for the variables in FG and SG, respectively.

• Crossload_FG, Crossload_FG Canonical cross-loadings for FG variables on the SG scores, and cross-loadings for SG variables on the FG scores, respectively.

• SigTest A dataframe with the correlation of the canonical pairs and hypothesis testing results.

• collinearity A list with the collinearity diagnostic for each group of variables.

References

Olivoto, T., V.Q. Souza, M. Nardino, I.R. Carvalho, M. Ferrari, A.J. Pelegrin, V.J. Szareski, and D. Schmidt. 2017. Multicollinearity in path analysis: a simple method to reduce its effects. Agron. J. 109:131-142. 10.2134/agronj2016.04.0196

Examples

# NOT RUN {
library(metan)

cc1 <- can_corr(data_ge2,
               FG = c(PH, EH, EP),
               SG = c(EL, ED, CL, CD, CW, KW, NR))


# Canonical correlations for each environment
cc3 <- data_ge2 %>%
       can_corr(FG = c(PH, EH, EP),
                SG = c(EL, ED, CL, CD, CW, KW, NR),
                by = ENV,
                verbose = FALSE)

# }