maxRgain (version 1.0.2)

rmaxa: Maximum admissible gain

Description

The maximum admissible genetic gain in one trait that can be achieved without decreasing any of the other traits, as a percentage of the overall mean. This function calculates the maximum admissible gains achieved in the specified traits.

Usage

rmaxa(
  traits,
  ref = NULL,
  clmin = 2,
  clmax,
  constraints = NULL,
  meanvec = NULL,
  criteria = NULL,
  data
)

Value

A list with the following components:

• gain with the gains of the several traits in each dimension

• selected_<trait> with the reference of the clones selected in the group of each dimension in each trait

Arguments

traits

A vector with the names of the columns in the data corresponding to the target traits to be optimized, i.e., those included in the objective function.

ref

Name of the reference column (e.g., genotype ID). Defaults to the first column.

clmin

An integer specifying the minimum group size. If omitted, equal to 2.

clmax

An integer specifying the maximum group size. If omitted, equal to clmin.

constraints

Vector with traits to which constraints apply. If omitted, all except ref are used.

meanvec

A named numeric vector of trait means; if omitted, data are assumed to be already normalized by the mean.

criteria

A named numeric vector indicating the selection criterion for each trait: 1 for traits to be increased, -1 for traits to be decreased. If omitted, all traits are assumed to be selected for increase.

data

A data frame comprising the input data consisting of the Predictors of genetic effects, which serve as the basis for the selection procedure.

References

Surgy, S., Cadima, J. & Gonçalves, E. Integer programming as a powerful tool for polyclonal selection in ancient grapevine varieties. Theor Appl Genet 138, 122 (2025). tools:::Rd_expr_doi("10.1007/s00122-025-04885-0")

Examples

mymeanvec <- c(yd = 3.517, pa = 12.760, ta = 4.495, ph = 3.927, bw = 1.653)
mytraits <- c("yd", "pa")
mycriteria <- c(yd = 1, pa = 1, ta = 1, ph = -1, bw = -1)
maxadm <- rmaxa(
   traits = mytraits,
   clmin = 7,
   clmax = 20,
   meanvec = mymeanvec,
   criteria = mycriteria,
   data = Gouveio
   )
maxadm