# Plots a heat map of computed LSD values for pairwise differences between predictions. Produces a heat-map plot of the computed LSD values for pairwise differences between predictions by multiplying the values stored in the `sed` component of an `alldiffs` object by the `t`-value for the `df` stored in the `tdf` attribute of the object. This component is generally a matrix whose rows and columns are labelled by the levels of one or more factors, the set of labels being the same for rows and columns. The `sections` argument allows multiple plots to be produced, one for each combination of the levels of the factors listed in `sections`. Otherwise, a single plot is produced for all observed combinations of the levels of the factors in the `classify` attribute for the `alldiffs.object`. The plots are produced using `plotLSDs.data.frame`. The order of plotting the levels of one of the factors indexing the predictions can be modified using `sort.alldiffs`. ```r plotLSDs(object, ...) ## S3 method for class 'alldiffs' plotLSDs(object, alpha = 0.05, sections = NULL, gridspacing = 0, factors.per.grid = 0, triangles = "both", title = NULL, axis.labels = TRUE, axis.text.size = 12, sep=",", colours = RColorBrewer::brewer.pal(3, "Set2"), ggplotFuncs = NULL, printPlot = TRUE, sortFactor = NULL, sortParallelToCombo = NULL, sortNestingFactor = NULL, sortOrder = NULL, decreasing = FALSE, ...) ``` ## Arguments - `object`: An `alldiffs.object` with an `sed` component that is not `NULL`. - `alpha`: A `numeric` giving the significance level for the LSD. - `sections`: A `character` listing the names of the factors that are to be used to break the plot into sections. A separate plot will be produced for each observed combination of the levels of these factors. - `gridspacing`: A `numeric` specifying the number(s) of rows and columns that form groups in the grid of differences. An alternative is to specify the `factors.per.grid` argument to have the grid spacings automatically calculated. Grids are most useful when two or more factors index the rows and columns. If a single, nonzero number, **k** say, is given then a grid line is placed after every **k**th row and column. If a vector of values is given then the number of grid lines is the length of the vector and the spacing between each is specified by the elements of the vector. - `factors.per.grid`: A `numeric` specifying the number of factors to include within each grid of differences. The `gridspacing` will then be computed based on the numbers of combinations observed within the levels of the remaining factors in a single plot. The `gridspacing` argument to this function will be ignored if `factors.per.grid` is greater than zero. Grids are most useful when two or more factors index the rows and columns of each plot. - `triangles`: A `character` indicating whether the plot should include the `lower`, `upper` or `both` triangle(s). - `title`: A `character` string giving the main title for the plot and to which is appended the levels combination of the sectioning factors, if any, for each plot. - `axis.labels`: A `logical` indicating whether a label is to be added to the x- and y-axes. If TRUE, the label is the comma-separated list of factors whose levels combinations are involved in the prediction differences for which the LSD values are calculated. - `axis.text.size`: A `numeric` giving the size of the `labels` on the axes of the `heatmap`. - `sep`: A `character` giving the characters separating the levels of different factors in the row and column names of the `sed` component. - `colours`: A vector of colours to be passed to the `ggplot` function `scale\_colour\_gradientn`. - `ggplotFuncs`: A `list`, each element of which contains the results of evaluating a `ggplot2` function. It is created by calling the `list` function with a `ggplot2` function call for each element. It is passed to `ggplot` via `plotLSDs.data.frame` to be applied in creating the `ggplot` object. - `printPlot`: A `logical` indicating whether or not the a plot is to be printed. This would be used when just the returned `data.frame` is required. - `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)]`. - `decreasing`: A `logical` passed to `order` that detemines whether the order for sorting the `alldiffs.object` components is for increasing or decreasing magnitude of the predicted values. - ``...``: Provision for passsing arguments to functions called internally - not used at present. ## Returns A `list` with components named `LSDs` and `plots`. The `LSDs` component contains the `data.frame` with the columns `Rows`, `Columns`, `LSDs`, `sections1` and `sections2`. This `data.frame` is formed using the `sed` component of `object` and is used by `plotLSDs.data.frame` in producng the plot. The `plots` component contains a list of `ggplot` objects, one for each plot produced. Multiple plots are stored in the `plots` component if the `sections` argument is set and the plots are are named for the levels combinations of the sections. ## Author(s) Chris Brien ## See Also `plotLSDs.data.frame`, `exploreLSDs`, `sort.alldiffs`, `subset.alldiffs`, `ggplot` ## Examples ```r ##Subset WaterRunoff data to reduce time to execute data(WaterRunoff.dat) tmp <- subset(WaterRunoff.dat, Date == "05-18" & Benches != "3") ##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= tmp) 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 LSD values for predictions obtained using asreml or lmerTest if (exists("TS.diffs")) { plotLSDs(TS.diffs, gridspacing = rep(c(3,4), c(4,2))) plotLSDs(TS.diffs, sections = "Sources", axis.labels = TRUE) } ```

plotLSDs.alldiffs function