lpdaCV.3D {lpda} | R Documentation |
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)
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. |
Value
lpda.3D
returns the prediction error rate classification.
Author(s)
Maria Jose Nueda, mj.nueda@ua.es
See Also
Examples
### RNAseq is a 3-dimensional array
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))