crmn (version 0.0.21)

normPred: Predict for normalization

Description

Predict the normalized data using a previously fitted normalization model.

Usage

normPred(normObj, newdata, factors = NULL, lg = TRUE, predfunc = predict, ...)

Arguments

normObj

the result from normFit

newdata

an ExpressionSet or a matrix (in which case the standards must be passed on via ...), possibly the same as used to fit the normalization model in order to get the fitted data.

factors

column names in the pheno data slot describing the biological factors. Or a design matrix.

lg

logical indicating that the data should be log transformed

predfunc

the function to use to get predicted values from the fitted object (only for crmn)

...

passed on to standardsPred, standardsFit, odestandards, analytes

Value

the normalized data

Details

Apply fitted normalization parameters to new data to get normalized data. Current can not only handle matrices as input for methods 'RI' and 'one'.

See Also

normFit

Examples

# NOT RUN {
data(mix)
nfit <- normFit(mix, "crmn", factor="type", ncomp=3)
normedData <- normPred(nfit, mix, "type")
slplot(pca(t(log2(exprs(normedData)))), scol=as.integer(mix$type))
## same thing
Y <- exprs(mix)
G <- with(pData(mix), model.matrix(~-1+type))
isIS <- fData(mix)$tag == 'IS'
nfit <- normFit(Y, "crmn", factors=G, ncomp=3, standards=isIS)
normedData <- normPred(nfit, Y, G, standards=isIS)
slplot(pca(t(log2(normedData))), scol=as.integer(mix$type))
# }