metan (version 1.15.0)

covcor_design: Variance-covariance matrices for designed experiments

Description

Compute variance-covariance and correlation matrices using data from a designed (RCBD or CRD) experiment.

Usage

covcor_design(.data, gen, rep, resp, design = "RCBD", by = NULL, type = NULL)

Arguments

.data

The data to be analyzed. It can be a data frame, 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.

type

What the matrices should return? Set to NULL, i.e., a list of matrices is returned. The argument type allow the following values 'pcor', 'gcor', 'rcor', (which will return the phenotypic, genotypic and residual correlation matrices, respectively) or 'pcov', 'gcov', 'rcov' (which will return the phenotypic, genotypic and residual variance-covariance matrices, respectively). Alternatively, it is possible to get a matrix with the means of each genotype in each trait, by using type = 'means'.

Value

An object of class covcor_design containing the following items:

• geno_cov The genotypic covariance.

• phen_cov The phenotypic covariance.

• resi_cov The residual covariance.

• geno_cor The phenotypic correlation.

• phen_cor The phenotypic correlation.

• resi_cor The residual correlation.

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

# NOT RUN {
library(metan)
# List of matrices
data <- subset(data_ge2, ENV == 'A1')
matrices <- covcor_design(data,
                          gen = GEN,
                          rep = REP,
                          resp = c(PH, EH, NKE, TKW))

# Genetic correlations
gcor <- covcor_design(data,
                      gen = GEN,
                      rep = REP,
                      resp = c(PH, EH, NKE, TKW),
                      type = 'gcor')

# Residual (co)variance matrix for each environment
rcov <- covcor_design(data_ge2,
                      gen = GEN,
                      rep = REP,
                      resp = c(PH, EH, CD, CL),
                      by = ENV,
                      type = "rcov")
# }
# NOT RUN {
# }