Multitrait index based on factor analysis and ideotype-design proposed by Rocha et al. (2018).
fai_blup( .data, use_data = "blup", DI = NULL, UI = NULL, SI = 15, mineval = 1, verbose = TRUE )
.data | An object of class |
---|---|
use_data | Define which data to use If |
DI, UI | A vector of the same length of |
SI | An integer (0-100). The selection intensity in percentage of the total number of genotypes. Defaults to 15. |
mineval | The minimum value so that an eigenvector is retained in the factor analysis. |
verbose | Logical value. If |
An object of class fai_blup
with the following items:
data The data (BLUPS) used to compute the index.
eigen The eigenvalues and explained variance for each axis.
FA The results of the factor analysis.
canonical_loadings The canonical loadings for each factor retained.
FAI A list with the FAI-BLUP index for each ideotype design.
selection_diferential A list with the selection differential for each ideotype design.
sel_gen The selected genotypes.
ideotype_construction A list with the construction of the ideotypes.
total_gain A list with the total gain for variables to be increased or decreased.
Rocha, J.R.A.S.C.R, J.C. Machado, and P.C.S. Carneiro. 2018. Multitrait index based on factor analysis and ideotype-design: proposal and application on elephant grass breeding for bioenergy. GCB Bioenergy 10:52-60. doi: 10.1111/gcbb.12443
Tiago Olivoto tiagoolivoto@gmail.com
# \donttest{ library(metan) mod <- waasb(data_ge, env = ENV, gen = GEN, rep = REP, resp = c(GY, HM))#>#>#>#>#> --------------------------------------------------------------------------- #> 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#> #> ----------------------------------------------------------------------------------- #> Principal Component Analysis #> ----------------------------------------------------------------------------------- #> eigen.values cumulative.var #> PC1 1.1 55.23 #> PC2 0.9 100.00 #> #> ----------------------------------------------------------------------------------- #> Factor Analysis #> ----------------------------------------------------------------------------------- #> FA1 comunalits #> GY -0.74 0.55 #> HM 0.74 0.55 #> #> ----------------------------------------------------------------------------------- #> Comunalit Mean: 0.5523038 #> Selection differential #> ----------------------------------------------------------------------------------- #> VAR Factor Xo Xs SD SDperc sense goal #> 1 GY 1 2.674242 2.594199 -0.08004274 -2.9931005 none 0 #> 2 HM 1 48.088286 48.005568 -0.08271774 -0.1720122 none 0 #> #> ----------------------------------------------------------------------------------- #> Selected genotypes #> G4 G9 #> -----------------------------------------------------------------------------------# }