calculateGCV-methods: Calculate the optimal B-spline model using generalized...

In dipenps/rnits: R Normalization and Inference of Time Series data

Calculate the optimal B-spline model using generalized cross-validation

Description

Calculate the optimal B-spline model using generalized cross-validation

Usage

calculateGCV(object, topcomp = 5)

## S4 method for signature 'Rnits'
calculateGCV(object, topcomp = 5)

Arguments

object	Rnits object
topcomp	The number of top eigenvectors to be used for computation

Details

The optimal B-spline model is chosen as the largest model that minimizes the cross validation error of the top N eigenvectors of each time series data.

Value

A list object with fields 'degree', 'df' for each time series data set.

Examples

# load pre-compiled expressionSet object for Ronen and Botstein yeast chemostat data
data(yeastchemostat)
rnitsobj = build.Rnits(yeastchemostat, logscale = TRUE, normmethod = 'Between')
opt_model <- calculateGCV(rnitsobj)
## Not run: 
rnitsobj <- fit(rnitsobj, gene.level = TRUE, model = opt.model)

## End(Not run)

dipenps/rnits documentation built on March 18, 2023, 7:30 p.m.