# Sorts the components in an `alldiffs.object` according to the predicted values associated with a factor. Sorts the rows of the components in an `alldiffs.object` (see `as.alldiffs`) that are `data.frames` and the rows and columns of those that are `matrices` according to the predicted values in the `predictions` component. These predicted values are generally obtained using `predict.asreml` by specifying a `classify` term comprised of one or more variables. Generally, the values associated with one variable are sorted in parallel within each combination of values of the other variables. When there is more than one variable in the `classify` term, the sorting is controlled using one or more of `sortFactor`, `sortParallelToCombo` and `sortOrder`. If there is only one variable in the `classify` then all components are sorted according to the order of the complete set of predictions. Note that `renewClassify.alldiffs` is called after sorting to ensure that the order of the rows and columns of the components is in standard order for the new variable order. ```r ## S3 method for class 'alldiffs' sort(x, decreasing = FALSE, classify = NULL, sortFactor = NULL, sortParallelToCombo = NULL, sortNestingFactor = NULL, sortOrder = NULL, ...) ``` ## Arguments - `x`: An `alldiffs.object`. - `decreasing`: A `logical` passed to `order` that detemines whether the order is for increasing or decreasing magnitude of the predicted values. - `classify`: A `character` string giving the variables that define the margins of the multiway table that was predicted. Multiway tables are specified by forming an interaction type term from the classifying variables, that is, separating the variable names with the `:` operator. If `NULL`, it will be obtained from the `classify` attribute of the `as.alldiffs` object supplied through `x`. - `sortFactor`: A `character` containing the name of the `factor` that indexes the set of predicted values that determines the sorting of the components. If there is only one variable in the `classify` term then `sortFactor` can be `NULL` and the order is defined by the complete set of predicted values. If there is more than one variable in the `classify` term then `sortFactor` must be set. In this case the `sortFactor` is sorted in the same order within each combination of the values of the `sortParallelToCombo` variables: the `classify` variables, excluding the `sortFactor`. There should be only one predicted value for each unique value of `sortFactor` within each set defined by a combination of the values of the `classify` variables, excluding the `sortFactor` `factor`. The order to use is determined by either `sortParallelToCombo` or `sortOrder`. - `sortParallelToCombo`: A `list` that specifies a combination of the values of the `factor`s and `numeric`s, excluding `sortFactor`, that are in `classify`. Each of the components of the supplied `list` is named for a `classify` variable and specifies a single value for it. The combination of this set of values will be used to define a subset of the predicted values whose order will define the order of `sortFactor`. Each of the other combinations of the values of the `factor`s and `numeric`s will be sorted in parallel. If `sortParallelToCombo` is `NULL` then the first value of each `classify` variable, except for the `sortFactor` `factor`, in the `predictions` component is used to define `sortParallelToCombo`. If there is only one variable in the `classify` then `sortParallelToCombo` is ignored. - `sortNestingFactor`: A `character` containing the name of the `factor` that defines groups of the `sortFactor` within which the predicted values are to be ordered. If there is only one variable in the `classify` then `sortNestingFactor` is ignored. - `sortOrder`: A `character` vector whose length is the same as the number of levels for `sortFactor` in the `predictions` component of the `alldiffs.object`. It specifies the desired order of the levels in the reordered components of the `alldiffs.object`. The argument `sortParallelToCombo` is ignored. The following creates a `sortOrder` vector `levs` for factor `f` based on the values in `x`: `levs <- levels(f)[order(x)]`. - ``...``: further arguments passed to or from other methods. Not used at present. ## Returns The `alldiffs.object` supplied with the following components, if present, sorted: `predictions`, `vcov`, `backtransforms`, `differences`, `p.differences` and `sed`. Also, the `sortFactor` and `sortOrder` attributes are set. ## Details The basic technique is to change the order of the levels of the `sortFactor` within the `predictions` and, if present, `backtransforms` components so that they are ordered for a subset of predicted values, one for each levels of the `sortFactor`. When the `classify` term consists of more than one variable then a subset of one combination of the values of variables other than the `sortFactor`, the `sortParallelToCombo` combination, must be chosen for determining the order of the `sortFactor` levels. Then the sorting of the rows (and columns) will be in parallel within each combination of the values of `sortParallelToCombo` variables: the `classify` term, excluding the `sortFactor`. ## Author(s) Chris Brien ## See Also `as.alldiffs`, `allDifferences.data.frame`, `print.alldiffs`, `sort.predictions.frame`, `renewClassify.alldiffs`, `redoErrorIntervals.alldiffs`, `recalcLSD.alldiffs`, `predictPlus.asreml`, `predictPresent.asreml` ## Examples ```r ##Halve WaterRunoff data to reduce time to execute data(WaterRunoff.dat) tmp <- subset(WaterRunoff.dat, Date == "05-18") ##Use asreml to get predictions and associated statistics ## Not run: #Analyse pH m1.asr <- asreml(fixed = pH ~ Benches + (Sources * (Type + Species)), random = ~ Benches:MainPlots, keep.order=TRUE, data= tmp) current.asrt <- as.asrtests(m1.asr, NULL, NULL) current.asrt <- as.asrtests(m1.asr) current.asrt <- rmboundary(current.asrt) m1.asr <- current.asrt$asreml.obj #Get predictions and associated statistics TS.diffs <- predictPlus.asreml(classify = "Sources:Type", asreml.obj = m1.asr, tables = "none", wald.tab = current.asrt$wald.tab, present = c("Type","Species","Sources")) #Use sort.alldiffs and save order for use with other response variables TS.diffs.sort <- sort(TS.diffs, sortFactor = "Sources", sortParallelToCombo = list(Type = "Control")) sort.order <- attr(TS.diffs.sort, which = "sortOrder") #Analyse Turbidity m2.asr <- asreml(fixed = Turbidity ~ Benches + (Sources * (Type + Species)), random = ~ Benches:MainPlots, keep.order=TRUE, data= tmp) current.asrt <- as.asrtests(m2.asr) #Use pH sort.order to sort Turbidity alldiffs object diffs2.sort <- predictPlus(m2.asr, classify = "Sources:Type", pairwise = FALSE, error.intervals = "Stand", tables = "none", present = c("Type","Species","Sources"), sortFactor = "Sources", sortOrder = sort.order) ## End(Not run) ## Use lmeTest and emmmeans to get predictions and associated statistics if (requireNamespace("lmerTest", quietly = TRUE) & requireNamespace("emmeans", quietly = TRUE)) { #Analyse pH m1.lmer <- lmerTest::lmer(pH ~ Benches + (Sources * (Type + Species)) + (1|Benches:MainPlots), data=na.omit(tmp)) TS.emm <- emmeans::emmeans(m1.lmer, specs = ~ Sources:Type) TS.preds <- summary(TS.emm) den.df <- min(TS.preds$df, na.rm = TRUE) ## Modify TS.preds to be compatible with a predictions.frame TS.preds <- as.predictions.frame(TS.preds, predictions = "emmean", se = "SE", interval.type = "CI", interval.names = c("lower.CL", "upper.CL")) ## Form an all.diffs object and check its validity TS.vcov <- vcov(TS.emm) TS.diffs <- allDifferences(predictions = TS.preds, classify = "Sources:Type", vcov = TS.vcov, tdf = den.df) validAlldiffs(TS.diffs) #Use sort.alldiffs and save order for use with other response variables TS.diffs.sort <- sort(TS.diffs, sortFactor = "Sources", sortParallelToCombo = list(Type = "Control")) sort.order <- attr(TS.diffs.sort, which = "sortOrder") #Analyse Turbidity m2.lmer <- lmerTest::lmer(Turbidity ~ Benches + (Sources * (Type + Species)) + (1|Benches:MainPlots), data=na.omit(tmp)) TS.emm <- emmeans::emmeans(m2.lmer, specs = ~ Sources:Type) TS.preds <- summary(TS.emm) den.df <- min(TS.preds$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, sorting it using the pH sort.order and check its validity TS.vcov <- vcov(TS.emm) TS.diffs2.sort <- allDifferences(predictions = TS.preds, classify = "Sources:Type", vcov = TS.vcov, tdf = den.df, sortFactor = "Sources", sortOrder = sort.order) validAlldiffs(TS.diffs2.sort) } ```

sort.alldiffs function