1d:T772,# Re-Scale results of 'VCA' or 'VCAinference' Function adjusts variance components (VC) and standard deviations (SD) and their respective confidence intervals of 'VCAinference' objects, and the 'VCAobj' sub-element. For 'VCA' objects the VC and SD values are adjusted as well as the fixed and random effects and the covariance-matrix of fixed effects. ```r reScale(obj, VarVC = TRUE) ``` ## Arguments - `obj`: (object) either of class 'VCA' or 'VCAinference' - `VarVC`: (logical) TRUE = variance-covariance matrix of the fitted model 'obj' will be computed and automatically re-scaled, FALSE = variance-covariance matrix will not be computed and re-scaled. This might cause wrong results in downstream analyses which require this matrix on the correct scale! Only use this option if computation time really matters! ## Returns (object) either of class 'VCA' or 'VCAinference', where results have been transformed back to the original scale of the response variable ## Examples ```r ## Not run: data(dataEP05A2_3) # 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, MME=TRUE) 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") ## End(Not run) ``` ## See Also `Scale` ## Author(s) Andre Schuetzenmeister andre.schuetzenmeister@roche.com

reScale function