# Automatically Scale Data Calling these Functions: 'anovaVCA', 'anovaMM', 'remlVCA' or 'remlMM' This function scales data before fitting a linear mixed model aiming to avoid numerical problems when numbers of the response variable are either very small or very large. It adds attribute "scale" to the resulting 'VCA'-object, which is used by function `reScale` to transform back the VCA-results of a `VCA` or `VCAinference` object that was previously scaled. ```r Scale(Fun, form, Data, ...) ``` ## Arguments - `Fun`: (expr, function, character) either a complete function call to one of "anovaVCA", "anovaMM", "remlVCA", "remlMM", a character string or just the function name without quotes (see example) - `form`: (formula) specifying the model to fitted by 'Fun' - `Data`: (data.frame) with all variables specified via 'Fun' - `...`: additional arguments applying to one of the four functions `anovaVCA`,`anovaMM`, `remlVCA`, `remlMM` ## Returns (object) of class 'VCA' which can be used as input for function `VCAinference` ## Details NOTE: Scaling is applied on the complete data set, without checking whether there are incomplete observations or not! ## Examples ```r ## Not run: data(dataEP05A2_3) # simulate very large numbers of the response dat3 <- dataEP05A2_3 dat3$y <- dat3$y * 1e8 # now try to fit 21-day model to this data fit <- anovaVCA(y~day/run, dat3) # now use 'Scale' function fit1 <- Scale("anovaVCA", y~day/run, dat3) fit2 <- Scale(anovaVCA, y~day/run, dat3) # also works fit3 <- Scale(anovaVCA(y~day/run, dat3)) # works as well # back to original scale (fit1 <- reScale(fit1)) (fit2 <- reScale(fit2)) (fit3 <- reScale(fit3)) # reference values fit0 <- anovaVCA(y~day/run, dataEP05A2_3, MME=TRUE) inf0 <- VCAinference(fit0, VarVC=TRUE) fit1 <- Scale(anovaVCA(y~day/run, dataEP05A2_3, MME=TRUE)) inf1 <- VCAinference(fit1, VarVC=TRUE) inf1 <- reScale(inf1) # compare to reference print(inf0, what="VC") print(inf1, what="VC") print(inf0, what="SD") print(inf1, what="SD") print(inf0, what="CV") print(inf1, what="CV") # now use REML-based estimation fit0 <- remlVCA(y~day/run, dataEP05A2_3) inf0 <- VCAinference(fit0) fit1 <- Scale("remlVCA", y~day/run, dataEP05A2_3) inf1 <- VCAinference(fit1) inf1 <- reScale(inf1) # compare to reference print(inf0, what="VC") print(inf1, what="VC") print(inf0, what="SD") print(inf1, what="SD") print(inf0, what="CV") print(inf1, what="CV") # scaling also works with by-processing data(VCAdata1) fit <- Scale(anovaVCA(y~(device+lot)/day/run, VCAdata1, by="sample")) reScale(fit) ## End(Not run) ``` ## See Also `reScale` ## Author(s) Andre Schuetzenmeister andre.schuetzenmeister@roche.com

Scale function