# Subsets the components in an `alldiffs.object` according to the supplied condition. Subsets each of the components of an `alldiffs.object`. The subset is determined by applying the condition to the `prediction` component to determine which of its rows are to be included in the subset. Then, if present, this subset is applied to the rows of `backtransforms` and to the rows and columns of `differences`, `p.differences` and `sed` components. In addition, if `sed` is present, `recalcLSD.alldiffs` is called to recalculate the values in the `LSD.frame` stored in the `LSD` component, with any arguments supplied via the `...` argument passed ot it. The `select` argument of `subset` is not implemented, but can be achieved for variables in the `classify` using the `rmClassifyVars` argument. ```r ## S3 method for class 'alldiffs' subset(x, subset = rep(TRUE, nrow(x$predictions)), rmClassifyVars = NULL, ...) ``` ## Arguments - `x`: An `alldiffs.object`. - `subset`: A `logical` that detemines rows of the `predictions` component of `x` to be included in the subset. By default allws are included. - `rmClassifyVars`: A `character` that contains the names of the variables in the `classify` attribute of `x` that are to be removed from the predictions `data.frame` and the names of the dimensions of the other components of `x`. In doing this, the combinations of the remaining `classify` variables must uniquely index the predictions. - ``...``: further arguments passed to `recalcLSD.alldiffs`. ## Returns An `alldiffs.object` with the following components of the supplied `alldiffs.object` subsetted, if present in the original object: `predictions`, `vcov`, `backtransforms`, `differences`, `p.differences` and `sed`. In addition, if `sed` is present, the `LSD.frame` in the `LSD` component will be recalculated. ## Author(s) Chris Brien ## See Also `as.alldiffs`, `allDifferences.data.frame`, `print.alldiffs`, `sort.alldiffs`, `redoErrorIntervals.alldiffs`, `recalcLSD.alldiffs`, `predictPlus.asreml`, `predictPresent.asreml` ## Examples ```r data(WaterRunoff.dat) ##Use asreml to get predictions and associated statistics ## Not run: asreml.options(keep.order = TRUE) #required for asreml-R4 only current.asr <- asreml(fixed = pH ~ Benches + (Sources * (Type + Species)), random = ~ Benches:MainPlots, keep.order=TRUE, data= WaterRunoff.dat) current.asrt <- as.asrtests(current.asr, NULL, NULL) TS.diffs <- predictPlus.asreml(classify = "Sources:Type", asreml.obj = current.asr, tables = "none", wald.tab = current.asrt$wald.tab, present = c("Type","Species","Sources")) ## 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(pH ~ Benches + (Sources * (Type + Species)) + (1|Benches:MainPlots), data=na.omit(WaterRunoff.dat)) TS.emm <- emmeans::emmeans(m1.lmer, specs = ~ Sources:Type) TS.preds <- summary(TS.emm) den.df <- min(TS.preds$df, na.rm = TRUE) ## Modify TS.preds to be compatible with a predictions.frame TS.preds <- as.predictions.frame(TS.preds, predictions = "emmean", se = "SE", interval.type = "CI", interval.names = c("lower.CL", "upper.CL")) ## Form an all.diffs object and check its validity TS.vcov <- vcov(TS.emm) TS.diffs <- allDifferences(predictions = TS.preds, classify = "Sources:Type", vcov = TS.vcov, tdf = den.df) validAlldiffs(TS.diffs) } ## Plot p-values for predictions obtained using asreml or lmerTest if (exists("TS.diffs")) { ##Use subset.alldiffs to select a subset of the alldiffs object TS.diffs.subs <- subset(TS.diffs, subset = grepl("R", Sources, fixed = TRUE) & Type %in% c("Control","Medicinal")) } ```

subset.alldiffs function