# Reorders and/or revises the factor levels using the order of old levels in `levels.order` and the new labels for the levels given in `newlabels`. The values in `levels.order` must be unique. Reorders and revises the levels and labels of a `factor`, in the `prediction` component of an `alldiffs.object`. The values in the `levels.order` vector should be the same as the levels in the existing `factor`, but the order can be changed. To revise the levels, specify the new levels in the `newlabels` vector and these will replace the corresponding value in the `levels.order` vector. The matching changes are made to the other components and attributes of the `alldiffs.object`. ```r ## S3 method for class 'alldiffs' facRecast(object, factor, levels.order = NULL, newlabels = NULL, ...) ``` ## Arguments - `object`: An `alldiffs.object`. - `factor`: A `character` containing the name of a `factor` in the `prediction` component of `object` whose levels and labels are to be recast. - `levels.order`: A `vector` of length `levels(factor)` containing the old levels in the new order for the factor being created. If `levels.order` is `NULL`, then the current levels of `levels(factor)` are used. - `newlabels`: A `vector` of length `levels(factor)` containing values to use in the revision. - `...`: Further arguments passed to the `factor` call creating the new `factor`. ## Returns A modified `alldiffs.object`. ## Author(s) Chris Brien ## See Also `as.alldiffs`, `allDifferences.data.frame`, `print.alldiffs`, `sort.alldiffs`, `facCombine.alldiffs`, `facRename.alldiffs`, `renewClassify.alldiffs`; `fac.recast` in package `dae`. ## Examples ```r data("Ladybird.dat") ## Use asreml to get predictions and associated statistics ## Not run: m1.asr <- asreml(logitP ~ Host*Cadavers*Ladybird, random = ~ Run, data = Ladybird.dat) current.asrt <- as.asrtests(m1.asr) HCL.pred <- asreml::predict.asreml(m1.asr, classify="Host:Cadavers:Ladybird", sed=TRUE) HCL.preds <- HCL.pred$pvals HCL.sed <- HCL.pred$sed HCL.vcov <- NULL wald.tab <- current.asrt$wald.tab den.df <- wald.tab[match("Host:Cadavers:Ladybird", rownames(wald.tab)), "denDF"] ## End(Not run) ## Use lmeTest and emmmeans to get predictions and associated statistics if (requireNamespace("lmerTest", quietly = TRUE) & requireNamespace("emmeans", quietly = TRUE)) { m1.lmer <- lmerTest::lmer(logitP ~ Host*Cadavers*Ladybird + (1|Run), data=Ladybird.dat) HCL.emm <- emmeans::emmeans(m1.lmer, specs = ~ Host:Cadavers:Ladybird) HCL.preds <- summary(HCL.emm) den.df <- min(HCL.preds$df) ## Modify HCL.preds to be compatible with a predictions.frame HCL.preds <- as.predictions.frame(HCL.preds, predictions = "emmean", se = "SE", interval.type = "CI", interval.names = c("lower.CL", "upper.CL")) HCL.vcov <- vcov(HCL.emm) HCL.sed <- NULL } ## Use the predictions obtained with either asreml or lmerTest if (exists("HCL.preds")) { ## Form an all.diffs object HCL.diffs <- allDifferences(predictions = HCL.preds, classify = "Host:Cadavers:Ladybird", sed = HCL.sed, vcov = HCL.vcov, tdf = den.df) ## Check the class and validity of the alldiffs object is.alldiffs(HCL.diffs) validAlldiffs(HCL.diffs) ## Recast the Ladybird and Host factors HCL.diffs <- facRecast(HCL.diffs, factor = "Ladybird", newlabels = c("none", "present")) HCL.diffs <- facRecast(HCL.diffs, factor = "Ladybird", levels.order = c("present", "none"), newlabels = c("yes","no")) HCL.diffs <- facRecast.alldiffs(HCL.diffs, factor = "Host", levels.order = c("trefoil", "bean")) ## Check the validity of HCL.diffs validAlldiffs(HCL.diffs) } ```

facRecast.alldiffs function