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Stability analysis and environment stratification

Source: R/ge_factanal.R
ge_factanal.Rd

[Stable]

This function computes the stability analysis and environmental stratification using factor analysis as proposed by Murakami and Cruz (2004).

Usage

ge_factanal(.data, env, gen, rep, resp, mineval = 1, verbose = TRUE)

Arguments

.data

The dataset containing the columns related to Environments, Genotypes, replication/block and response variable(s)

env

The name of the column that contains the levels of the environments.

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 variable(s). To analyze multiple variables in a single procedure use, for example, resp = c(var1, var2, var3).

mineval

The minimum value so that an eigenvector is retained in the factor analysis.

verbose

Logical argument. If verbose = FALSE the code will run silently.

Value

An object of class ge_factanal with the following items:

  • data: The data used to compute the factor analysis.

  • cormat: The correlation matrix among the environments.

  • PCA: The eigenvalues and explained variance.

  • FA: The factor analysis.

  • env_strat: The environmental stratification.

  • KMO: The result for the Kaiser-Meyer-Olkin test.

  • MSA: The measure of sampling adequacy for individual variable.

  • communalities: The communalities.

  • communalities.mean: The communalities' mean.

  • initial.loadings: The initial loadings.

  • finish.loadings: The final loadings after varimax rotation.

  • canonical.loadings: The canonical loadings.

  • scores.gen: The scores for genotypes for the first and second factors.

References

Murakami, D.M.D., and C.D.C. Cruz. 2004. Proposal of methodologies for environment stratification and analysis of genotype adaptability. Crop Breed. Appl. Biotechnol. 4:7-11.

See also

superiority(), ecovalence(), ge_stats(), ge_reg()

Author

Tiago Olivoto, tiagoolivoto@gmail.com

Examples

# \donttest{
library(metan)
model <- ge_factanal(data_ge2,
                     env = ENV,
                     gen = GEN,
                     rep = REP,
                     resp = PH)
# }