can_corr function

Canonical correlation analysis

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

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).

Returns

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.

Examples

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)

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. tools:::Rd_expr_doi("10.2134/agronj2016.04.0196")

Author(s)

Tiago Olivoto tiagoolivoto@gmail.com