# Plots a map of the supplied errors that occur in using the computed LSD values for pairwise differences between predictions. Produces a plot of the errors that have been supplied in a `data.frame`. The `data.frame` includes two factors whose levels specify, for each LSD result, which combinations of factor levels are being compared. The function `plotLSDerrors.alldiffs` produces such `data.frame`s. ```r ## S3 method for class 'data.frame' plotLSDerrors(object, LSDresults = "LSDresults", x, y, alpha = 0.05, triangles = "both", gridspacing = 0, title = NULL, axis.labels = NULL, axis.text.size = 12, colours = c("white","blue","red","grey"), ggplotFuncs = NULL, printPlot = TRUE, ...) ``` ## Arguments - `object`: A `data.frame` containing the three columns specified by `LSDresults`, `x` and `y`. - `LSDresults`: A `character` giving the name of the column in `object` that contains the LSDresults values to be plotted. The column should be a `character` or `factor` with values or levels that are a subset of `Ok`,`FN`, `FP` and `na`. - `x`: A `character` giving the name of the column in `object` that contains the factor whose levels index the LSD values that are to be plotted in the same column. - `y`: A `character` giving the name of the column in `object` that contains the labels of the LSD values that are to be plotted as the rows. - `alpha`: A `numeric` giving the significance level for the LSD. - `triangles`: A `character` indicating whether the plot should include the `lower`, `upper` or `both` triangle(s). Here it is only used to adjust gridlines for the omission of the diagonal. - `gridspacing`: A `numeric` specifying the number(s) of rows and columns that form groups in the grid of differences. This is 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. - `title`: A `character` string giving the main title for the plot. - `axis.labels`: A `character` string giving the label to use for both the x- and y-axis. - `axis.text.size`: A `numeric` giving the size of the `labels` on the axes of the `heatmap`. - `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. These functions are 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 `ggplot` object is required. - ``...``: Provision for passing arguments to functions called internally - not used at present. ## Returns An object of class "`ggplot`", which can be plotted using `print` or otherwise manipulated. ## Author(s) Chris Brien ## See Also `plotLSDs.data.frame`, `plotLSDs.alldiffs`, `exploreLSDs`, `ggplot` ## Examples ```r ##Subset 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: 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")) { LSDresults <- within(reshape2::melt(TS.diffs$p.differences), { Var1 <- factor(Var1, levels=dimnames(TS.diffs$p.differences)[[1]]) Var2 <- factor(Var2, levels=levels(Var1)) }) names(LSDresults) <- c("Rows","Columns","LSDresults") plotLSDerrors(LSDresults, x = "Rows", y = "Columns", gridspacing = rep(c(3,4), c(4,2))) } ```

plotLSDerrors.data.frame function