Computes the multi-trait genotype-ideotype distance index (MGIDI). MGIDI can be seen as the multi-trait stability index (Olivoto et al., 2019) computed with weight for mean performance equals to 100. The MGIDI indes is computed as follows:
MGIDI_i = _j = 1^f(F_ij - F_j)^2
where MGIDI_i is the multi-trait genotype-ideotype distance index for the ith genotype; F_ij is the score of the ith genotype in the jth factor (i = 1, 2, ..., g; j = 1, 2, ..., f), being g and f the number of genotypes and factors, respectively, and F_j is the jth score of the ideotype. The genotype with the lowest MGIDI is then closer to the ideotype and therefore presents desired values for all the analyzed traits.
mgidi(
.data,
use_data = "blup",
SI = 15,
mineval = 1,
ideotype = NULL,
use = "complete.obs",
verbose = TRUE
)Define which data to use if .data is an object of
class gamem. Defaults to "blup" (the BLUPs for genotypes).
Use "pheno" to use phenotypic means instead BLUPs for computing the
index.
An integer (0-100). The selection intensity in percentage of the total number of genotypes.
The minimum value so that an eigenvector is retained in the factor analysis.
A vector of length nvar where nvar is the
number of variables used to plan the ideotype. Use 'h' to indicate
the traits in which higher values are desired or 'l' to indicate the
variables in which lower values are desired. For example, ideotype =
c("h, h, h, h, l") will consider that the ideotype has higher values for
the first four traits and lower values for the last trait. If .data
is a model fitted with the functions gafem() or
gamem(), the order of the traits will be the declared in the
argument resp in those functions.
The method for computing covariances in the presence of missing
values. Defaults to complete.obs, i.e., missing values are handled
by casewise deletion.
If verbose = TRUE (Default) then some results are
shown in the console.
An object of class mgidi 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.
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 in all retained factors.
scores_ide The scores for the ideotype in all retained factors.
gen_ide The distance between the scores of each genotype with the ideotype.|
MGIDI The multi-trait genotype-ideotype distance index.
contri_fac The relative contribution of each factor on the MGIDI value. The lower the contribution of a factor, the close of the ideotype the variables in such factor are.
contri_fac_rank, contri_fac_rank_sel The rank for the contribution of each factor for all genotypes and selected genotypes, respectively.
sel_dif The selection differential for the variables.
stat_gain A descriptive statistic for the selection gains. The minimum, mean, confidence interval, standard deviation, maximum, and sum of selection gain values are computed. If traits have negative and positive desired gains, the statistics are computed for by strata.
sel_gen The selected genotypes.
Olivoto, T., A.D.C. L\'ucio, J.A.G. da silva, B.G. Sari, and M.I. Diel. 2019. Mean performance and stability in multi-environment trials II: Selection based on multiple traits. Agron. J. 111:2961-2969. 10.2134/agronj2019.03.0220
# NOT RUN {
library(metan)
model <- gamem(data_g,
gen = GEN,
rep = REP,
resp = c(NR, KW, CW, CL, NKE, TKW, PERK, PH))
# Selection for increase all variables
mgidi_model <- mgidi(model)
# plot the contribution of each factor on the MGIDI index
plot(mgidi_model, type = "contribution")
# }
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