This function implements the weighting method between mean performance and stability (Olivoto et al., 2019) considering different parametric and non-parametric stability indexes.
mps( .data, env, gen, rep, resp, block = NULL, by = NULL, random = "gen", performance = "blupg", stability = "waasb", ideotype_mper = NULL, ideotype_stab = NULL, wmper = NULL, 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
|
performance | Wich considers as mean performance. Either |
stability | The stability method. One of the following:
|
ideotype_mper, ideotype_stab | The new maximum value after rescaling the
response variable/stability index. By default, all variables in |
wmper | The weight for the mean performance. By default, all variables
in |
verbose | Logical argument. If |
An object of class mps
with the following items.
observed
: The observed value on a genotype-mean basis.
performance
: The performance for genotypes (BLUPs or BLUEs)
performance_res
: The rescaled values of genotype's performance,
considering ideotype_mper
.
stability
: The stability for genotypes, chosen with argument stability
.
stability_res
: The rescaled values of genotype's stability, considering
ideotype_stab
.
mps_ind
: The mean performance and stability for the traits.
h2
: The broad-sense heritability for the traits.
perf_method
: The method for measuring genotype's performance.
wmper
: The weight for the mean performance.
sense_mper
: The goal for genotype's performance (l
= lower, h
= higher).
stab_method
: The method for measuring genotype's stability.
wstab
: The weight for the mean stability.
sense_stab
: The goal for genotype's stability (l
= lower, h
= higher).
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Tiago Olivoto tiagoolivoto@gmail.com
# \donttest{ library(metan) # The same approach as mtsi() # mean performance and stability for GY and HM # mean performance: The genotype's BLUP # stability: the WAASB index (lower is better) # weights: equal for mean performance and stability model <- mps(data_ge, env = ENV, gen = GEN, rep = REP, resp = everything())#> Evaluating trait GY |====================== | 50% 00:00:00 Evaluating trait HM |============================================| 100% 00:00:00#>#>#>#>#> --------------------------------------------------------------------------- #> P-values for Likelihood Ratio Test of the analyzed traits #> --------------------------------------------------------------------------- #> model GY HM #> COMPLETE NA NA #> GEN 1.11e-05 5.07e-03 #> GEN:ENV 2.15e-11 2.27e-15 #> --------------------------------------------------------------------------- #> All variables with significant (p < 0.05) genotype-vs-environment interaction#>#># The mean performance and stability after rescaling model$mps_ind#> # A tibble: 10 x 3 #> GEN GY HM #> <chr> <dbl> <dbl> #> 1 G1 57.6 56.2 #> 2 G10 0 35.0 #> 3 G2 59.9 17.8 #> 4 G3 95.5 67.9 #> 5 G4 45.7 58.6 #> 6 G5 40.0 61.1 #> 7 G6 45.9 85.5 #> 8 G7 45.2 51.3 #> 9 G8 77.3 90.6 #> 10 G9 16.3 58.7# }