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Plot the multitrait index based on factor analysis and ideotype-design proposed by Rocha et al. (2018).

Usage

# S3 method for fai_blup
plot(
  x,
  ideotype = 1,
  SI = 15,
  radar = TRUE,
  arrange.label = FALSE,
  size.point = 2.5,
  size.line = 0.7,
  size.text = 10,
  col.sel = "red",
  col.nonsel = "black",
  ...
)

Arguments

x

An object of class waasb

ideotype

The ideotype to be plotted. Default is 1.

SI

An integer (0-100). The selection intensity in percentage of the total number of genotypes.

radar

Logical argument. If true (default) a radar plot is generated after using coord_polar().

arrange.label

Logical argument. If TRUE, the labels are arranged to avoid text overlapping. This becomes useful when the number of genotypes is large, say, more than 30.

size.point

The size of the point in graphic. Defaults to 2.5.

size.line

The size of the line in graphic. Defaults to 0.7.

size.text

The size for the text in the plot. Defaults to 10.

col.sel

The colour for selected genotypes. Defaults to "red".

col.nonsel

The colour for nonselected genotypes. Defaults to "black".

...

Other arguments to be passed from ggplot2::theme().

Value

An object of class gg, ggplot.

References

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

Author

Tiago Olivoto tiagoolivoto@gmail.com

Examples

# \donttest{
library(metan)

mod <- waasb(data_ge,
             env = ENV,
             gen = GEN,
             rep = REP,
             resp = c(GY, HM))
#> Evaluating trait GY |======================                      | 50% 00:00:00 
Evaluating trait HM |============================================| 100% 00:00:01 

#> Method: REML/BLUP
#> Random effects: GEN, GEN:ENV
#> Fixed effects: ENV, REP(ENV)
#> Denominador DF: Satterthwaite's method
#> ---------------------------------------------------------------------------
#> 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

FAI <- fai_blup(mod,
                DI = c('max, max'),
                UI = c('min, min'))
#> 
#> -----------------------------------------------------------------------------------
#> 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 increase    0
#> 2  HM      1 48.088286 48.005568 -0.08271774 -0.1720122 increase    0
#> 
#> -----------------------------------------------------------------------------------
#> Selected genotypes
#> G4 G9
#> -----------------------------------------------------------------------------------
plot(FAI)

# }