addPairwiseLetters.alldiffs: Adds a column of letters to a predictions.frame, there being...

In asremlPlus: Augments 'ASReml-R' in Fitting Mixed Models and Packages Generally in Exploring Prediction Differences

Adds a column of letters to a predictions.frame, there being a unique letter for each set of predictions that are not significantly different.

Description

Given an alldiffs.object with a p.differences component, identify the sets of predictions such that the predictions within a set are not significantly different and assign a lowercase letter to these, and only these, predictions.

Usage

## S3 method for class 'alldiffs'
addPairwiseLetters(alldiffs.obj, within = NULL, 
                   alpha = 0.05, ...)

Arguments

alldiffs.obj	An alldiffs.object.
within	A character (vector) of variables names, being the names of the factors or numerics in the classify; for each combination of the values of the factors and numerics, the letters indicating the differences between the predictions within the combination are added to the predictions.frame in the alldiffs.object.
alpha	A numeric specifying the significance level for t-test for judging whether a pair of predictions is different.
...	Provision for passing arguments to functions called internally - not used at present.

Value

A alldiffs.object whose predictions.frame has a column named pairwiseSignif that contains the letters that specify the sets of predictions that are not significantly different

Author(s)

Chris Brien

See Also

asremlPlus-package

Examples

data(WaterRunoff.dat)

##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= WaterRunoff.dat)
current.asrt <- as.asrtests(current.asr, NULL, NULL)
TS.diffs <- predictPlus(classify = "Sources:Type", 
                        asreml.obj = current.asr, 
                        wald.tab = current.asrt$wald.tab, 
                        present = c("Sources", "Type", "Species"))

## 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)
}  

## Add significance letters for predictions obtained using asreml or lmerTest
if (exists("TS.diffs"))
{
  ## Obtain the letters that indicate the significances with each Type
  TS.diffs <- addPairwiseLetters(TS.diffs, within = "Type")
  
  ## Plot the predictions with the letters
  library(ggplot2)
  ggplot(data = TS.diffs$predictions, 
         mapping = aes(x = Sources, y = predicted.value, 
                       colour = Sources, fill = Sources)) + 
    facet_grid(cols = vars(Type)) + 
    geom_bar(stat = "identity") +
    labs(y = "pH") + 
    geom_errorbar(aes(ymin=lower.Confidence.limit, 
                      ymax=upper.Confidence.limit,
                      width=0.5), colour = "black") +
    geom_text(aes(y = (upper.Confidence.limit + 0.025*upper.Confidence.limit), 
                  label = pairwiseSignif), colour = "black") +
    theme(legend.position = "none",
          axis.text.x = element_text(angle=90))
}

asremlPlus documentation built on July 10, 2026, 9:07 a.m.