IBCF.MTME (version 1.6-0)

CV.RandomPart: Cross-Validation with Random Partitions

Description

This method consists of randomly dividing the training data set and the test data set. For each division, the approximation function is adjusted from the training data and calculates the output values for the test data set. The result corresponds to the arithmetic mean of the values obtained for the different divisions.

Usage

CV.RandomPart(DataSet, NPartitions = 10, PTesting = 0.35,
  Traits.testing = NULL, Set_seed = NULL)

Arguments

DataSet

data.frame The data set object is a data.frame object that contains 4 columns in the Tidy data format: $Line is the Line or genotype identifier, and the name of this column could change. $Env is the name of the evaluated environment (s). $Trait is the name of the evaluated trait (s). $Response Variable response obtained for the row corresponding to line and environment.

NPartitions

integer Number of Partitions for the Cross-Validation. Is 10 by default.

PTesting

Double Percentage of Testing for the Cross-Validation. Is 0.35 by default.

Traits.testing

character By default is null and use all the traits to fit the model, else only part of the traits specified be used to fit the model.

Set_seed

integer Number of seed for reproducible research. Is NULL by default.

Value

List A list object with length of NPartitions, every index has a matrix \(n \times x\), where \(n\) is the number of NLines and \(x\) is the number of NEnv \(\times\) NTraits. The values inside is 1 for training and 2 for testing.

Examples

# NOT RUN {
  library(IBCF.MTME)
  data('Wheat_IBCF')

  CV.RandomPart(Wheat_IBCF)
  CV.RandomPart(Wheat_IBCF, NPartitions = 10)
  CV.RandomPart(Wheat_IBCF, Traits.testing = 'DH')
  CV.RandomPart(Wheat_IBCF, NPartitions = 10, PTesting = .35)
  CV.RandomPart(Wheat_IBCF, NPartitions = 10, Traits.testing = 'DH')
  CV.RandomPart(Wheat_IBCF, NPartitions = 10, PTesting = .35, Set_seed = 5)
  CV.RandomPart(Wheat_IBCF, NPartitions = 10, PTesting = .35, Traits.testing = 'DH')
  CV.RandomPart(Wheat_IBCF, NPartitions = 10, PTesting = .35, Traits.testing = 'DH', Set_seed = 5 )
# }