Fit a model which describes the variation of the labeled internal standards from the biological factors.
standardsFit(object, factors, ncomp = NULL, lg = TRUE, fitfunc = lm, ...)an ExpressionSet or a matrix. Note
that if you pass amatrix have to specify the identity of
the standards by passing the appropriate argument to
standards.
the biological factors described in the pheno data
slot if object is an ExpressionSet or a design
matrix if object is a matrix.
number of PCA components to use. Determined by
cross-validation if left NULL
logical indicating that the data should be log transformed
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
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.
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.
makeX, standardsPred
# NOT RUN {
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)
# }
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