WAASB: Weighted Average of Absolute Scores for the BLUP'S GxE...

Description Usage Arguments Details Value Author(s) See Also Examples

View source: R/WAASB.R

Description

Compute the Weighted Average of Absolute Scores for quantifying the stability in multienvironment trials using mixed-effect models.

Usage

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WAASB(.data, resp, gen, env, rep, mresp = NULL, wresp = NULL,
      random = "gen", prob = 0.05,  verbose = TRUE)

Arguments

.data

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

resp

The response variable(s). To analyze multiple variables in a single procedure a vector of variables may be used. For example resp = c(var1, var2, var3).

gen

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

env

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

rep

The name of the column that contains the levels of the replications/blocks.

mresp

A numeric vector of the same length of resp. The mresp will be the new maximum value after reescaling. By default, all variables in resp are rescaled so that de maximum value is 100 and the minimum value is 0.

wresp

The weight for the response variable(s) for computing the WAASBY index. Must be a numeric vector of the same length of resp. Defatul is 50, i.e., equal weights for stability and mean performance.

random

The effects of the model assumed to be random. Default is random = "gen" (genotype and genotype-vs-environment as random effects. Other values allowed are random = "env" (environment, genotype-vs-environment and block-within-environment random effects) or random = "all" all effects except the intercept are assumed to be random effects.

prob

The probability for estimating confidence interval for BLUP's prediction.

verbose

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

Details

This function compute the weighted average of absolute scores considering all principal component axis from the Singular Value Decomposition (SVD) of the BLUP'S GxE effects matrix generated by a linear mixed-effect model. The main advantage of this procedure in relation to the WAAS.AMMI function is that random effects can be included in the model. In addition, unbalanced datasets can also be modeled.

Value

The function returns the results in a list for each analyzed variable. For each variable, the following objects are returned.

individual

A within-environments ANOVA considering a fixed-effect model.

fixed

Test for fixed effects.

random

Variance components for random effects.

LRT

The Likelihood Ratio Test for the random effects.

model

A data frame with the response variable, the scores of all Principal Components, the estimates of Weighted Average of Absolute Scores, and WAASY (the index that consider the weights for stability and productivity in the genotype ranking.

blupGEN

The estimated BLUPS for genotypes (If random = "gen" or random = "all")

BLUPenv

The estimated BLUPS for environments, (If random = "env" or random = "all").

BLUPge

The estimated BLUPS of all genotypes in all environments "BLUPij".

PCA

The results of Principal Component Analysis with eigenvalues and explained variance of BLUP-interaction matrix.

MeansGxE

The phenotypic means of genotypes in the environments, with observed, predicted and OLS residual prediction.

Details

A list summarizing the results. The following information are showed. WgtResponse, the weight for the response variable in estimating WAASB, WgtWAAS the weight for stability, Ngen the number of genotypes, Nenv the number of environments, OVmean the overall mean, Min the minimum observed (returning the genotype and environment), Max the maximum observed, Max the maximum observed, MinENV the environment with the lower mean, MaxENV the environment with the larger mean observed, MinGEN the genotype with the lower mean, MaxGEN the genotype with the larger.

ESTIMATES

A list with the following values: GEV the genotype-by-environment variance (and percentage of phenotypic variance); GV the genotypic variance (and percentage of phenotypic variance); EV the environmental variance;RV the residual variance (and percentage of phenotypic variance); FV the phenotypic variance; h2g the heritability of the trait; GEr2 the coefficient of determination of the interaction effects; h2mg the heritability of the mean; AccuGen the selective accuracy; rge the genotype-environment correlation; CVg the genotypic coefficient of variation; CVr the residual coefficient of variation; CVratio the ratio between genotypic and residual coefficient of variation.

residuals

The residuals of the model.

Author(s)

Tiago Olivoto [email protected]

See Also

WAAS.AMMI

Examples

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library(METAAB)

# Genotypes as random effects and equal weights for both

# response variable and stability

model <-  WAASB(data_ge,
               resp = GY,
               gen = GEN,
               env = ENV,
               rep = REP,
               wresp = 70)

# Higher weight for response variable

model2 <- WAASB(data_ge,
               resp = GY,
               gen = GEN,
               env = ENV,
               rep = REP,
               wresp = 65)

# Environment as random effects analyzing more than one variables
# considering that smaller values of HM are better and higher
# values of GY are better, assigning a larger weight for the GY
# and a smaller weight for HM when computing WAASBY index.

model3 <- WAASB(data_ge,
                random = "env",
                resp = c(GY, HM),
                gen = GEN,
                env = ENV,
                rep = REP,
                mresp = c(100, 0),
                wresp = c(60, 40))

TiagoOlivoto/WAASB documentation built on April 1, 2019, 10:25 a.m.