mahala_design function

Mahalanobis distance from designed experiments

Compute the Mahalanobis distance using data from an experiment conducted in a randomized complete block design or completely randomized design.

mahala_design(
  .data,
  gen,
  rep,
  resp,
  design = "RCBD",
  by = NULL,
  return = "distance"
)

Arguments

• .data: The dataset containing the columns related to Genotypes, replication/block and response variables, possible with grouped data passed from dplyr::group_by().
• gen: The name of the column that contains the levels of the genotypes.
• rep: The name of the column that contains the levels of the replications/blocks.
• resp: The response variables. For example resp = c(var1, var2, var3).
• design: The experimental design. Must be RCBD or CRD.
• 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.
• return: What the function return? Default is 'distance', i.e., the Mahalanobis distance. Alternatively, it is possible to return the matrix of means return = 'means', or the variance-covariance matrix of residuals return = 'covmat'.

Returns

A symmetric matrix with the Mahalanobis' distance. If .data is a grouped data passed from dplyr::group_by() then the results will be returned into a list-column of data frames.

Examples

library(metan)
maha <- mahala_design(data_g,
                      gen = GEN,
                      rep = REP,
                      resp = everything(),
                      return = "covmat")

# Compute one distance for each environment (all numeric variables)
maha_group <- mahala_design(data_ge,
                           gen = GEN,
                           rep = REP,
                           resp = everything(),
                           by = ENV)

# Return the variance-covariance matrix of residuals
cov_mat <- mahala_design(data_ge,
                        gen = GEN,
                        rep = REP,
                        resp = c(GY, HM),
                        return = 'covmat')

Author(s)

Tiago Olivoto tiagoolivoto@gmail.com