metan (version 1.2.1)

fai_blup: Multi-trait selection index

Description

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

Usage

fai_blup(.data, DI, UI, SI = NULL, mineval = 1, verbose = TRUE)

Arguments

.data

An object of class waasb or a two-way table with genotypes in the rows and traits in columns. In the last case the row names must contain the genotypes names.

DI, UI

A vector of the same length of .data to construct the desirable (DI) and undesirable (UI) ideotypes. For each element of the vector, allowed values are 'max', 'min', 'mean', or a numeric value. Use a comma-separated vector of text. For example, DI = c("max, max, min, min").

SI

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

mineval

The minimum value so that an eigenvector is retained in the factor analysis.

verbose

Logical value. If TRUE some results are shown in console.

Value

An object of class fai_blup with the following items:

• data The data (BLUPS) used to compute the index.

• 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.

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: doi:10.1111/gcbb.12443

Examples

# NOT RUN {
library(metan)

mod <- waasb(data_ge,
             env = ENV,
             gen = GEN,
             rep = REP,
             resp = c(GY, HM))

FAI <- fai_blup(mod,
                SI = 15,
                DI = c('max, max'),
                UI = c('min, min'))

# Or using the pipe operator %>%

FAI <- data_ge2 %>%
       waasb(ENV, GEN, REP, c(KW, NKE, PH, EH)) %>%
       fai_blup(DI = c('max, max, max, min'),
                UI = c('min, min, min, max'),
                SI = 15)
# }

Run the code above in your browser using DataCamp Workspace