Package 'maxRgain'

Gouveio variety dataset

Description

A dataset containing the Empirical Best Linear Unbiased Predictors (EBLUP) of genotypic effects obtained by the fitting of linear mixed models with the observations from polyclonal selection field trials of variety Gouveio.

Usage

Gouveio

Format

An object of class data.frame with 150 rows and 6 columns.

Details

Clone

the genotype label

yd

EBLUP of the genotypic effect of yield

pa

EBLUP of the genotypic effect of potential alcool

ta

EBLUP of the genotypic effect of total acidity

bw

EBLUP of the genotypic effect of berry weight

ph

EBLUP of the genotypic effect of pH

---

Integer programming calculations

Description

This function maximizes the predicted genetic gain in the selection of groups of genotypes based on the predictors of genotypic effects.

Usage

polyclonal(
  traits,
  ref = NULL,
  clmin = 2,
  clmax,
  dmg = NULL,
  meanvec = NULL,
  criteria = NULL,
  data
)

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.
dmg	A data.frame with three columns defining constraints: trait names; constraints signs (">=", "<=" or "=="); and right-hand side values of the constraints.
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.

Value

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

Note

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.

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). 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",  "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)

---

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
)

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.

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

Note

The order of traits must be consistent across traits, constraints, meanvec, and criteria. Both meanvec and criteria must include values for all traits specified in traits and constraints. If constraints is omitted, all traits in the dataset are considered; in that case, meanvec and criteria must provide values for all of them.

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). 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

---

Maximum possible gain

Description

The maximum possible is the mean of the EBLUPs of the genotypic effects of the best n clones in a given trait, as a percentage of the overall mean. This function calculates the maximum possible gain achieved in the specified traits.

Usage

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

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.
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.

Value

A list with the following components:

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

• selected with the reference of the clones selected in the group of each dimension

Note

The order of the traits must be consistent across traits, meanvec, and criteria.

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). doi:10.1007/s00122-025-04885-0

Examples

mymeanvec <- c(pa = 12.760)
mytraits <- c("pa")
maxpos <- rmaxp(
   traits = mytraits,
   clmin = 7,
   clmax = 20,
   meanvec = mymeanvec,
   data = Gouveio
   )
maxpos

---

Update method for polyclonal results

Description

Allows updating any argument of a previous polyclonal() call without rewriting the full call. Defaults are preserved for arguments not provided.

Usage

## S3 method for class 'polyresult'
update(object, ...)

Arguments

object	A polyresult object returned by polyclonal().
...	Arguments to update in the original polyclonal() call.

Value

A new polyresult object with updated arguments applied.

Examples

#' Original call

selections <- polyclonal(traits = c("yd", "pa", "ta", "ph", "bw"),
                  clmin = 7, clmax = 15,
                  dmg = "base",
                  meanvec = c(yd = 3.517, pa = 12.760, ta = 4.495, ph = 3.927, bw = 1.653),
                  criteria = c(yd = 1, pa = 1, ta = 1, ph = -1, bw = -1),
                  data = Gouveio)

selections
# Update clmax
selupdate1 <- update(selections, clmax = 10)

selupdate1

# Update dmg
selupdate2 <- update(selupdate1, dmg = data.frame(
                     lhs = c("yd", "pa", "ta", "ph", "bw"),
                     rel = c(">=", ">=", ">=", ">=", ">="),
                     rhs = c(25, 0, 0, 0, 0)
                     )
                   )

selupdate2

---