plotPvalues.alldiffs {asremlPlus} | R Documentation |
Plots a heat map of p-values for pairwise differences between predictions.
Description
Produces a heat-map plot of the p-values for pairwise differences between
predictions that is stored in the p.differences
component of an
all.diffs
object. This 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
for the alldiffs.object
. The plots are produced using
plotPvalues.data.frame
. The order of plotting the levels of
one of the factors indexing the predictions can be modified using
sort.alldiffs
.
Usage
plotPvalues(object, ...)
## S3 method for class 'alldiffs'
plotPvalues(object, sections = NULL,
gridspacing = 0, factors.per.grid = 0,
show.sig = FALSE, alpha = 0.10,
sig.size = 3, sig.colour = "black",
sig.face = "plain", sig.family = "",
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 a p.differences component that is not
NULL .
|
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.
|
show.sig |
A logical that specifies whether asterisks indicating the level
of significance are to be added to the plot. If they are then
‘***’ indicates that p \leq 0.001 ,
‘**’ that 0.001 < p \leq 0.01 ,
‘*’ that 0.01 < p \leq 0.05
‘.’ that 0.05 < p \leq 0.10 . The last is only
included for alpha = 0.10.
|
alpha |
A numeric giving the significance level for testing
pairwise differences; must be 0.05 or 0.10.
|
sig.size |
A numeric specifying the size, in pts, of the
significance asterisks.
|
sig.colour |
A character specifying the colour to use for the
significance asterisks.
|
sig.face |
A character specifying the font face for the significance
asterisks ("plain" , "italic" , "bold" , "bold.italic" ).
|
sig.family |
A character specifying the font family for the significance
asterisks. The font families that are available depends on the system. For font
families other than the basic Postscript fonts, see the extrafont package.
|
triangles |
A character indicating whether the plot should include the
lower , upper or both traingle(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 p-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 p.differences component.
|
colours |
A vector of 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 plotPvalues.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 pvalues
and plots
.
The pvalues
component contains the data.frame
with the columns Rows
,
Columns
, p
, sections1
and sections2
. This data.frame
is
formed using the sed
component of object
and is used by
plotPvalues.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
plotPvalues.data.frame
, allDifferences.data.frame
,
sort.alldiffs
, subset.alldiffs
, ggplot
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 p-values for predictions obtained using asreml or lmerTest
if (exists("TS.diffs"))
{
plotPvalues(TS.diffs, gridspacing = rep(c(3,4), c(4,2)), show.sig = TRUE)
plotPvalues(TS.diffs, sections = "Sources", show.sig = TRUE, axis.labels = TRUE)
}
[Package
asremlPlus version 4.4.40
Index]