gamem_met  R Documentation 
Genotype analysis in multienvironment trials using mixedeffect or randomeffect models.
The nature of the effects in the model is chosen with the argument
random
. By default, the experimental design considered in each
environment is a randomized complete block design. If block
is
informed, a resolvable alphalattice design (Patterson and Williams, 1976) is
implemented. The following six models can be fitted depending on the values
of random
and block
arguments.
Model 1: block = NULL
and random = "gen"
(The
default option). This model considers a Randomized Complete Block Design in
each environment assuming genotype and genotypeenvironment interaction as
random effects. Environments and blocks nested within environments are
assumed to fixed factors.
Model 2: block = NULL
and random = "env"
. This
model considers a Randomized Complete Block Design in each environment
treating environment, genotypeenvironment interaction, and blocks nested
within environments as random factors. Genotypes are assumed to be fixed
factors.
Model 3: block = NULL
and random = "all"
. This
model considers a Randomized Complete Block Design in each environment
assuming a randomeffect model, i.e., all effects (genotypes, environments,
genotypevsenvironment interaction and blocks nested within environments)
are assumed to be random factors.
Model 4: block
is not NULL
and random = "gen"
. This model considers an alphalattice design in each environment
assuming genotype, genotypeenvironment interaction, and incomplete blocks
nested within complete replicates as random to make use of interblock
information (Mohring et al., 2015). Complete replicates nested within
environments and environments are assumed to be fixed factors.
Model 5: block
is not NULL
and random = "env"
. This model considers an alphalattice design in each environment
assuming genotype as fixed. All other sources of variation (environment,
genotypeenvironment interaction, complete replicates nested within
environments, and incomplete blocks nested within replicates) are assumed
to be random factors.
Model 6: block
is not NULL
and random = "all"
. This model considers an alphalattice design in each environment
assuming all effects, except the intercept, as random factors.
gamem_met( .data, env, gen, rep, resp, block = NULL, by = NULL, random = "gen", prob = 0.05, verbose = TRUE )
.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 a vector of variables may be used. For example 
block 
Defaults to 
by 
One variable (factor) to compute the function by. It is a shortcut
to 
random 
The effects of the model assumed to be random. Defaults to

prob 
The probability for estimating confidence interval for BLUP's prediction. 
verbose 
Logical argument. If 
An object of class waasb
with the following items for each
variable:
fixed Test for fixed effects.
random Variance components for random effects.
LRT The Likelihood Ratio Test for the random effects.
BLUPgen The random effects and estimated BLUPS for genotypes (If
random = "gen"
or random = "all"
)
BLUPenv The random effects and estimated BLUPS for environments,
(If random = "env"
or random = "all"
).
BLUPint The random effects and estimated BLUPS of all genotypes in all environments.
MeansGxE The phenotypic means of genotypes in the environments.
modellme The mixedeffect model of class lmerMod
.
residuals The residuals of the mixedeffect model.
model_lm The fixedeffect model of class lm
.
residuals_lm The residuals of the fixedeffect model.
Details A list summarizing the results. The following information
are shown: Nenv
, the number of environments in the analysis;
Ngen
the number of genotypes in the analysis; Mean
the grand
mean; SE
the standard error of the mean; SD
the standard
deviation. CV
the coefficient of variation of the phenotypic means,
estimating WAASB, Min
the minimum value observed (returning the
genotype and environment), Max
the maximum value observed (returning
the genotype and environment); 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 tibble with the genetic parameters (if random = "gen"
or random = "all"
) with the following columns: Phenotypic variance
the phenotypic variance; Heritability
the broadsense
heritability; GEr2
the coefficient of determination of the interaction
effects; h2mg
the heritability on the mean basis;
Accuracy
the selective accuracy; rge
the genotypeenvironment
correlation; CVg
the genotypic coefficient of variation; CVr
the residual coefficient of variation; CV ratio
the ratio between
genotypic and residual coefficient of variation.
formula The formula used to fit the mixedmodel.
Tiago Olivoto tiagoolivoto@gmail.com
Olivoto, T., A.D.C. L\'ucio, J.A.G. da silva, V.S. Marchioro, V.Q. de Souza, and E. Jost. 2019. Mean performance and stability in multienvironment trials I: Combining features of AMMI and BLUP techniques. Agron. J. 111:29492960. doi: 10.2134/agronj2019.03.0220
Mohring, J., E. Williams, and H.P. Piepho. 2015. Interblock information: to recover or not to recover it? TAG. Theor. Appl. Genet. 128:154154. doi: 10.1007/s0012201525300
Patterson, H.D., and E.R. Williams. 1976. A new class of resolvable incomplete block designs. Biometrika 63:8392.
mtsi()
waas()
get_model_data()
plot_scores()
library(metan) #===============================================================# # Example 1: Analyzing all numeric variables assuming genotypes # # as random effects # #===============================================================# model < gamem_met(data_ge, env = ENV, gen = GEN, rep = REP, resp = everything()) # Distribution of random effects (first variable) plot(model, type = "re") # Genetic parameters get_model_data(model, "genpar") #===============================================================# # Example 2: Unbalanced trials # # assuming all factors as random effects # #===============================================================# un_data < data_ge %>% remove_rows(1:3) %>% droplevels() model2 < gamem_met(un_data, env = ENV, gen = GEN, rep = REP, random = "all", resp = GY) get_model_data(model2)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.