polyclonal | R Documentation |
This function maximizes the predicted genetic gain in the selection of groups of genotypes based on the predictors of genotypic effects.
polyclonal(
traits,
ref = NULL,
clmin = 2,
clmax,
dmg = NULL,
meanvec = NULL,
criteria = NULL,
data
)
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 |
dmg |
A |
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. |
A list with the following components:
gain
with the gains of the several traits in each dimension
selected
with the reference os the clones selected in the group of each dimension
The order of traits must be consistent across traits
, dmg
, meanvec
, and criteria
. Both meanvec
and criteria
must include values for all traits specified in traits
and dmg
.
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). \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1007/s00122-025-04885-0")}
mymeanvec <- c(yd = 3.517, pa = 12.760, ta = 4.495, ph = 3.927, bw = 1.653)
mytraits <- c("yd", "pa", "ta", "ph", "bw")
mydmg <- data.frame(
lhs = c("yd", "pa", "ta", "ph", "bw"),
rel = c(">=", ">=", ">=", ">=", ">="),
rhs = c(20, 3, 3, 1, 2)
)
mycriteria <- c(yd = 1, pa = 1, ta = 1, ph = -1, bw = -1)
selections <- polyclonal(
traits = mytraits,
clmin = 7,
clmax = 20,
dmg = mydmg,
meanvec = mymeanvec,
criteria = mycriteria,
data = Gouveio
)
selections
summary(selections)
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