phen_varcov: Phenotypic Variance-Covariance Analysis

In selection.index: Analysis of Selection Index in Plant Breeding

Phenotypic Variance-Covariance Analysis

Description

Phenotypic Variance-Covariance Analysis

Usage

phen_varcov(
  data,
  genotypes,
  replication,
  columns = NULL,
  main_plots = NULL,
  design_type = c("RCBD", "LSD", "SPD"),
  method = c("REML", "Yates", "Healy", "Regression", "Mean", "Bartlett")
)

Arguments

data	traits to be analyzed
genotypes	vector containing genotypes/treatments (sub-plot treatments in SPD)
replication	vector containing replication/blocks (RCBD) or rows (LSD)
columns	vector containing columns (required for Latin Square Design only)
main_plots	vector containing main plot treatments (required for Split Plot Design only)
design_type	experimental design type: "RCBD" (default), "LSD" (Latin Square), or "SPD" (Split Plot)
method	Method for missing value imputation: "REML" (default), "Yates", "Healy", "Regression", "Mean", or "Bartlett"

Value

A Phenotypic Variance-Covariance Matrix

Examples

# RCBD example
phen_varcov(data = seldata[, 3:9], genotypes = seldata$treat, replication = seldata$rep)

# Latin Square Design example (requires columns parameter)
# phen_varcov(data=lsd_data[,3:7], genotypes=lsd_data$treat,
#            replication=lsd_data$row, columns=lsd_data$col, design_type="LSD")

# Split Plot Design example (requires main_plots parameter)
# phen_varcov(data=spd_data[,3:7], genotypes=spd_data$subplot,
#            replication=spd_data$block, main_plots=spd_data$mainplot, design_type="SPD")

selection.index documentation built on March 9, 2026, 1:06 a.m.