internal: Internal functions

In PopVar: Genomic Breeding Tools: Genetic Variance Prediction and Cross-Validation

Description

Internal functions generally not to be called by the user.

Usage

par_position(crossing.table, par.entries)

par_name(crossing.mat, par.entries)

tails(GEBVs, tail.p)

maf_filt(G)

XValidate_nonInd(
  y.CV = NULL,
  G.CV = NULL,
  models.CV = NULL,
  frac.train.CV = NULL,
  nCV.iter.CV = NULL,
  burnIn.CV = NULL,
  nIter.CV = NULL
)

XValidate_Ind(
  y.CV = NULL,
  G.CV = NULL,
  models.CV = NULL,
  nFold.CV = NULL,
  nFold.CV.reps = NULL,
  burnIn.CV = NULL,
  nIter.CV = NULL
)

calc_marker_effects(
  M,
  y.df,
  models = c("rrBLUP", "BayesA", "BayesB", "BayesC", "BL", "BRR"),
  nIter,
  burnIn
)

Arguments

crossing.table	The crossing table.
par.entries	The parent entries.
crossing.mat	The crossing matrix.
GEBVs	The genomic estimated breeding values.
tail.p	The proportion from the population to select.
G	The marker genotypes
y.CV	The phenotypes for cross-validation.
G.CV	The marker genotypes for cross-validation.
models.CV	The models for cross-validation.
frac.train.CV	The fraction of data to use as training data in cross-validation.
nCV.iter.CV	The number of iterations of cross-validation.
burnIn.CV	The burn-in number for cross-validation.
nIter.CV	The number of iterations for Bayesian models in cross-validation.
nFold.CV	The number of folds in k-fold cross-validation.
nFold.CV.reps	The number of replications of k-fold cross-validation.
M	The marker matrix.
y.df	The phenotype data.
models	The models to use.
nIter	The number of iterations.
burnIn	The burn-in rate.

PopVar documentation built on Feb. 8, 2021, 1:06 a.m.