R: Plots a heat map of computed LSD values for pairwise...

plotLSDs.alldiffs {asremlPlus}

R Documentation

Plots a heat map of computed LSD values for pairwise differences between predictions.

Description

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.

Usage

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 kth 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.

Value

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

Examples

##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)
  
}

[Package asremlPlus version 4.4.35 Index]