# Predictions from a Model Fitted by `fitLMM` Model returns fitted values in case `newdata` is NULL or evaluates the fitted model employing user-specified data `newdata`. The default is that fitted values incorporate fixed effects and random effects, leaving out the (conditional) residuals only. If the interest lies in constraining predictions to the fixed effects only set `re=NA` or incorporate just part of the random variability specifying distinct random effects (see `re`. ```r ## S3 method for class 'VCA' predict(object, newdata = NULL, re = NULL, allow.new.levels = FALSE, ...) ``` ## Arguments - `object`: (VCA) object fitted via function `fitLMM` - `newdata`: (data.frame) with all variables required for the specified prediction, i.e. the default settings require all variables of the original model, in case of `re=NA`, only variables corresponding to fixed effects are required. - `re`: (character) if NULL (default) all random effects will be included, to restrict predictions to the fixed effects use `re=NA`, for a subset of random effects included in predictions use any valid random effects specification, i.e. `object$random` - `allow.new.levels`: (logical) if new levels (no part of the original fitted model) in newdata are allowed. If FALSE (default), such new values in newdata will trigger an error; if TRUE, then the prediction will use the unconditional (population-level) values for data with previously unobserved levels (or NAs). - `...`: additional arguments passdo or from other methods ## Returns (numeric) vector of prediction results ## Examples ```r ## Not run: # fit LMM with fixed lot and device effects and test for lot-differences data(VCAdata1) datS5 <- subset(VCAdata1, sample==5) fitS5 <- fitLMM(y~(lot+device)/(day)/(run), datS5, "anova") fitS5 # fitted values including fixed and random effects pred0 <- predict(fitS5) pred0 # sanity check: all(round(pred0 + resid(fitS5) - datS5$y, 12) == 0) # restrict to fixed effects predict(fitS5, re=NA) # restrict to fixed effects and dayly random effects # see required names fitS5$random predict(fitS5, re="lot:device:day") # check against original 'lmer'-predictions # use version from VCA-package (ordinary data.frame) data(Orthodont, package="VCA") Ortho <- Orthodont Ortho$age2 <- Ortho$age-11 # use exactly the same data, same ordering Ortho <- orderData(Ortho, distance ~ Sex * age2 + (Subject) * age2) fit.fitLMM <- fitLMM(distance ~ Sex * age2 + (Subject) * age2, Ortho, "reml") library(lme4) fit.lmer <- lmer(distance ~ Sex + age2 + Sex:age2 + (age2 | Subject), Ortho) # check fitted value first (fixed + random effects) predict(fit.lmer) predict(fit.fitLMM) # restrict to fixed part only predict(fit.lmer, re.form=NA) predict(fit.fitLMM, re=NA) # user-specified 'newdata' newdata <- Ortho[45:54,] newdata$age2 <- newdata$age2 + 5 # include fixed and random effects predict(fit.lmer, newdata) predict(fit.fitLMM, newdata) # generate new data newdata <- Ortho[45:54,] newdata$age2 <- newdata$age2 + 5 # predict on newdata using fixed and random effects predict(fit.lmer, newdata) predict(fit.fitLMM, newdata) # restrict prediction to random Subject effects predict(fit.lmer, newdata, re.form=~(1|Subject)) predict(fit.fitLMM, newdata, re="Subject") # restrict prediction to random per-Subject slope predict(fit.lmer, newdata, re.form=~(age2-1|Subject)) predict(fit.fitLMM, newdata, re="age2:Subject") ## End(Not run) ``` ## Author(s) Andre Schuetzenmeister andre.schuetzeneister@roche.com

predict.VCA function