# Forms an `alldiffs.object` from the supplied predictions, along with those statistics, associated with the predictions and their pairwise differences, that have been supplied. Creates an `alldiffs.object` that consists of a `list` containing the following components: `predictions`, `vcov`, `differences`, `p.differences`, `sed`, `LSD` and `backtransforms`. Predictions must be supplied to the function while the others will be set only if they are supplied; those not supplied are set to `NULL`. It also has attributes `response`, `response.title`, `term`, `classify`, `tdf`, `tdf`, `alpha`, `sortFactor` and `sortOrder`. which will be set to the values supplied or `NULL` if none are supplied. ```r as.alldiffs(predictions, vcov = NULL, differences = NULL, p.differences = NULL, sed = NULL, LSD = NULL, backtransforms = NULL, response = NULL, response.title = NULL, term = NULL, classify = NULL, tdf = NULL, alpha = 0.05, sortFactor = NULL, sortOrder = NULL) ``` ## Arguments - `predictions`: A `predictions.frame`, being a `data.frame` beginning with the variables classifying the predictions and also containing columns named `predicted.value`, `standard.error` and `est.status`; each row contains a single predicted value. It may also contain columns for the lower and upper limits of error intervals for the predictions. Note that the names `standard.error` and `est.status` have been changed to `std.error` and `status` in the `pvals` component produced by `asreml-R4`; if the new names are in the `data.frame` supplied to `predictions`, they will be returned to the previous names. - `differences`: A `matrix` containing all pairwise differences between the predictions; it should have the same number of rows and columns as there are rows in `predictions`. - `p.differences`: A `matrix` containing p-values for all pairwise differences between the predictions; each p-value is computed as the probability of a t-statistic as large as or larger than the observed difference divided by its standard error. The degrees of freedom of the t distribution for computing it are computed as the denominator degrees of freedom of the F value for the fixed term, if available; otherwise, the degrees of freedom stored in the attribute `tdf` are used; the matrix should be of the same size as that for `differences`. - `sed`: A `matrix` containing the standard errors of all pairwise differences between the predictions; they are used in computing the p-values. - `vcov`: A `matrix` containing the variance matrix of the predictions; it is used in computing the variance of linear transformations of the predictions. - `LSD`: An `LSD.frame` containing the mean, minimum and maximum LSD for determining the significance of pairwise differences, as well as an assigned LSD and a measure of the accuracy of the LSD. If `LSD` is `NULL` then the `LSD.frame` stored in the `LSD` component will be calculated and the values of `LSDtype`, `LSDby` and `LSDstatistic` added as attributes of the `alldiffs.object`. The LSD for a single prediction assumes that any predictions to be compared are independent; this is not the case if residual errors are correlated. - `backtransforms`: A `data.frame` containing the backtransformed values of the predicted values that is consistent with the `predictions` component, except that the column named `predicted.value` is replaced by one called `backtransformed.predictions`. Any `error.interval` values will also be the backtransformed values. Each row contains a single predicted value. - `response`: A `character` specifying the response variable for the predictions. It is stored as an attribute to the `alldiffs.object`. - `response.title`: A `character` specifying the title for the response variable for the predictions. It is stored as an attribute to the `alldiffs.object`. - `term`: A `character` string giving the variables that define the term that was fitted using `asreml` and that corresponds to `classify`. It only needs to be specified when it is different to `classify`; it is stored as an attribute of the `alldiffs.object`. It is likely to be needed when the fitted model includes terms that involve both a `numeric` covariate and a `factor` that parallel each other; the `classify` would include the covariate and the `term` would include the `factor`. - `classify`: A character string giving the variables that define the margins of the multiway table used in the prediction. Multiway tables are specified by forming an interaction type term from the classifying variables, that is, separating the variable names with the `:` operator. It is stored as an attribute to the `alldiffs.object`. - `tdf`: an `integer` specifying the degrees of freedom of the standard error. It is used as the degrees of freedom for the t-distribution on which p-values and confidence intervals are based. It is stored as an attribute to the `alldiffs.object`. - `alpha`: A `numeric` giving the significance level for LSDs or one minus the confidence level for confidence intervals. It is stored as an attribute to the `alldiffs.object`. - `sortFactor`: A `character` containing the name of the `factor` that indexes the set of predicted values that determined the sorting of the components. - `sortOrder`: A `character` vector that is the same length as the number of levels for `sortFactor` in the `predictions` component of the `alldiffs.object`. It specifies the order of the levels in the reordered components of the `alldiffs.object`. The following creates a `sortOrder` vector `levs` for factor `f` based on the values in `x`: `levs <- levels(f)[order(x)]`. ## Returns An S3-class `alldiffs.object`. Also, see `predictPlus.asreml` for more information. ## Author(s) Chris Brien ## See Also `asremlPlus-package`, `alldiffs.object`, `is.alldiffs`, `as.alldiffs`, `print.alldiffs`, `sort.alldiffs`, `subset.alldiffs`, `allDifferences.data.frame`, `renewClassify.alldiffs`, `redoErrorIntervals.alldiffs`, `recalcLSD.alldiffs`, `predictPlus.asreml`, `plotPredictions.data.frame`, `predictPresent.asreml` ## Examples ```r data(Oats.dat) ## Use asreml to get predictions and associated statistics ## Not run: m1.asr <- asreml(Yield ~ Nitrogen*Variety, random=~Blocks/Wplots, data=Oats.dat) current.asrt <- as.asrtests(m1.asr) Var.pred <- asreml::predict.asreml(m1.asr, classify="Nitrogen:Variety", sed=TRUE) if (getASRemlVersionLoaded(nchar = 1) == "3") Var.pred <- Var.pred$predictions Var.preds <- Var.pred$pvals Var.sed <- Var.pred$sed Var.vcov <- NULL ## End(Not run) ## Use lmerTest and emmmeans to get predictions and associated statistics if (requireNamespace("lmerTest", quietly = TRUE) & requireNamespace("emmeans", quietly = TRUE)) { m1.lmer <- lmerTest::lmer(Yield ~ Nitrogen*Variety + (1|Blocks/Wplots), data=Oats.dat) Var.emm <- emmeans::emmeans(m1.lmer, specs = ~ Nitrogen:Variety) Var.preds <- summary(Var.emm) den.df <- min(Var.preds$df) ## Modify Var.preds to be compatible with a predictions.frame Var.preds <- as.predictions.frame(Var.preds, predictions = "emmean", se = "SE", interval.type = "CI", interval.names = c("lower.CL", "upper.CL")) Var.vcov <- vcov(Var.emm) Var.sed <- NULL } ## Use the predictions obtained with either asreml or lmerTest if (exists("Var.preds")) { ## Form an all.diffs object Var.diffs <- as.alldiffs(predictions = Var.preds, classify = "Nitrogen:Variety", sed = Var.sed, vcov = Var.vcov, tdf = den.df) ## Check the class and validity of the alldiffs object is.alldiffs(Var.diffs) validAlldiffs(Var.diffs) } ```

as.alldiffs function