standardsPred: Predict effect for new data (or get fitted data)

In crmn: CCMN and Other Normalization Methods for Metabolomics Data

Description

Predicted values for the standards

Usage

standardsPred(model, newdata, factors, lg = TRUE, ...)

Arguments

model	result from standardsFit
newdata	an ExpressionSet or matrix with new data (or the data used to fit the model to get the fitted data)
factors	the biological factors described in the pheno data slot if object is an ExpressionSet or a design matrix if object is a matrix.
lg	logical indicating that the data should be log transformed
...	passed on to standards and analytes

Details

There is often unwanted variation in among the labeled internal standards which is related to the experimental factors due to overlapping peaks etc. This predicts this effect given a model of the overlapping variance. The prediction is given by XhatIS=XIS-XIS*B

Value

the corrected data

Author(s)

Henning Redestig

See Also

makeX, standardsFit

Examples

data(mix)
fullFit <- standardsFit(mix, "type", ncomp=3)
sfit <- standardsFit(mix[,-1], "type", ncomp=3)
pred <- standardsPred(sfit, mix[,1], "type")
cor(scores(sfit$fit$pc)[1,], scores(fullFit$fit$pc)[1,])
## could just as well have been done as
Y <- exprs(mix)
G <- model.matrix(~-1+mix$type)
isIS <- fData(mix)$tag == 'IS'
fullFit <- standardsFit(Y, G, ncomp=3, standards=isIS)
sfit    <- standardsFit(Y[,-1], G[-1,], ncomp=3,
                        standards=isIS)
pred <- standardsPred(sfit, Y[,1,drop=FALSE], G[1,,drop=FALSE], standards=isIS)
cor(scores(sfit$fit$pc)[1,], scores(fullFit$fit$pc)[1,])

crmn documentation built on March 26, 2020, 8:35 p.m.