standardsFit: Standards model
In crmn: CCMN and Other Normalization Methods for Metabolomics Data

Description Usage Arguments Details Value Author(s) See Also Examples

Fit a model which describes the variation of the labeled internal standards from the biological factors.

1	standardsFit(object, factors, ncomp = NULL, lg = TRUE, fitfunc = lm, ...)

`object`	an `ExpressionSet` or a `matrix`. Note that if you pass a`matrix` have to specify the identity of the standards by passing the appropriate argument to `standards`.
`factors`	the biological factors described in the pheno data slot if `object` is an `ExpressionSet` or a design matrix if `object` is a `matrix`.
`ncomp`	number of PCA components to use. Determined by cross-validation if left `NULL`
`lg`	logical indicating that the data should be log transformed
`fitfunc`	the function that creates the model fit for normalization, must use the same interfaces as `lm`.
`...`	passed on to `Q2`, `pca` (if pcaMethods > 1.26.0), `standards` and `analytes`

There is often unwanted variation in among the labeled internal standards which is related to the experimental factors due to overlapping peaks etc. This function fits a model that describes that overlapping variation using a scaled and centered PCA / multiple linear regression model. Scaling is done outside the PCA model.

a list containing the PCA/MLR model, the recommended number of components for that model, the standard deviations and mean values and Q2/R2 for the fit.

Henning Redestig

makeX, standardsPred

data(mix)
sfit <- standardsFit(mix, "type", ncomp=3)
slplot(sfit$fit$pc)
## same thing
Y <- exprs(mix)
G <- model.matrix(~-1+mix$type)
isIS <- fData(mix)$tag == 'IS'
sfit <- standardsFit(Y, G, standards=isIS, ncomp=3)