lpda (version 1.2.0)

lpdaCV.3D: Crossvalidation procedure for lpda3D evaluation

Description

lpdaCV.3D evaluates the error rate classification with a crossvalidation procedure

Usage

lpdaCV.3D(data, group, scale = FALSE, pfac = FALSE, nfac = 2, nstart = 10, seed=2,
                       CV = "ktest", ntest = 10, R = 10, f1 = NULL, f2 = NULL)

Value

lpda.3D returns the prediction error rate classification.

Arguments

data

Array containing data. Individuals in the first mode, variables in the second mode and third mode with time or similar.

group

Vector with the variable group.

scale

Logical indicating if it is required standardize data.

pfac

Logical indicating if Parafac Analysis is required

nfac

Number of factors for Parafac Analysis. By default it is 2.

nstart

Number of random starts for multiway analysis.

seed

A single value to reproduce same results in multiway methods. If NULL the start will be random.

CV

Crossvalidation mode: loo "leave one out" or ktest: that leaves k in the test set.

ntest

Number of samples to evaluate in the test-set.

R

Number of times that the error is evaluated.

f1

Vector with weights for individuals of the first group. If NULL they are equally weighted.

f2

Vector with weights for individuals of the second group. If NULL they are equally weighted.

Author

Maria Jose Nueda,

See Also

lpda.3D, lpdaCV

Examples

### RNAseq is a 3-dimensional array
  # \donttest{
  data(RNAseq)
  group = as.factor(rep(c("G1","G2"), each = 10))
  lpdaCV.3D(RNAseq, group , CV = "ktest", R=5, ntest=5, pfac=TRUE, nfac=c(2,10))
  # }