valipars: Generate Control Parameters for Resampling

In mt: Metabolomics Data Analysis Toolbox

Generate Control Parameters for Resampling

Description

Generate the control parameters for resampling process.

Usage

valipars(sampling="cv", niter=10, nreps=10, strat=FALSE,div = 2/3) 

Arguments

sampling	Sampling scheme. Valid options are: loocv. Leave-one-out cross-validation cv. Cross-validation (default) rand. Randomised validation (holdout) boot. Bootstrap
niter	Number of iteration or repeat for validation.
nreps	Number of replications in each iteration.
strat	A logical value indicating whether the stratification should be applied to cv, rand and boot.
div	Proportion of data used for training in randomised validation method.

Details

valipars provides a list of control parameters for the resampling or validation in the process of accuracy evaluation or feature selection process.

Value

An object of class valipars containing all the above parameters (either the defaults or the user specified values).

Author(s)

Wanchang Lin

See Also

trainind

Examples

## generate control parameters for the re-sampling scheme with 5-fold 
## cross-validation and iteration of 10 times
valipars(sampling = "cv", niter = 10, nreps = 5)

## generate control parameters for the re-sampling scheme with 
## 25-replication bootstrap and iteration of 100 times
valipars(sampling = "boot", niter = 100, nreps = 25,strat=TRUE)

## generate control parameters for the re-sampling scheme with 
## leave-one-out cross-validation
valipars(sampling = "loocv")

mt documentation built on June 22, 2024, 12:24 p.m.