AquaBPsim (version 0.0.1)

groupmating: Group mating

Description

Function to determine which full sib families are produced in a group mating design.

Usage

groupmating(
  gen,
  batch = 0,
  batch_m = NA,
  batch_f = NA,
  No,
  contr_m,
  contr_f,
  distribution = "Gamma",
  shape = 0.75,
  scale = 0.11,
  selected = 1
)

Arguments

gen

The generations of the sires and dams

batch

The batches of the sires and dams. Default is 0.

batch_m

The batch of the sires. Default is NA. If batch_m is specified, batch_f also needs to be specified and the parameter batch is not used.

batch_f

The batch of the dams. Default is NA. If batch_f is specified, batch_m also needs to be specified and the parameter batch is not used.

No

The total number of offspring of all matings.

contr_m

Proportion of sires that contribute to the offspring.

contr_f

Proportion of dams that contribute to the offspring.

distribution

The distribution from which the contributions are drawn. Options are: "Gamma" (default) and "Uniform"

shape

The shape of the gamma distribution. Default is 0.75.

scale

The scale of the gamma distribution. Default is 0.11.

selected

The value in ped$selected of the selected sires and dams. Default is 1.

Value

The output is a data frame with for each full sib family the sire and dam and the number of offspring per full sib family.

Details

By default, the contribution of the sires and dams that do reproduce come from a gamma distribution. The default shape and scale of the gamma distribution are 0.75 and 0.11, respectively. A uniform distribution can also be specified for the contributions of the sires and dams. If not all sires and dams should contribute to the offspring, then the sires and dams that are going to reproduce are randomly chosen. The output is a dataframe called Mating with the sire, dam and the size of each full sib family.

The sires and dams can come from multiple batches or generations. In that case, a vector of batches or generations need to be provided.

Examples

# NOT RUN {
{ped <- founderpopgroup(Nm=60,
                     Nf=60,
                     Nm2=120,
                     Nf2=120,
                     Nbatch = 4,
                     batch2 = c(-3,-2,-1,0),
                     Ntraits=2,
                     TraitsIndex = 2,
                     Rgen= matrix(c(1.00   , 0.48,
                                    0.48   , 1.00),
                                 nrow = 2),
                     Rcom= matrix(c(1.00  , 0,
                                    0   , 1.00),
                                 nrow = 2),
                     Rres= matrix(c(1.00   , 0.32,
                                    0.32   , 1.00),
                                 nrow = 2),
                     mean=c(50,500),
                     a_var=c(200,8000),
                     c_var=c(0,0),
                     e_var= c(250,12000))
                     
 Mating <- groupmating(gen = 0,
                       batch=-3,
                       No=1000,
                       contr_m = 0.5,
                       contr_f = 0.5)
}
# }