asremlPlus (version 4.2-32)

recalcLSD.alldiffs: Adds or recalculates the LSD component of an alldiffs.object.

Description

Given an alldiffs.object, adds or recalculate its LSD component.

Usage

# S3 method for alldiffs
recalcLSD(alldiffs.obj, meanLSD.type = "overall", LSDby = NULL, 
          alpha = 0.05, ...)

Arguments

alldiffs.obj

An alldiffs.object.

meanLSD.type

A character string determining whether the mean LSD stored is (i) the overall mean, based on the square root of the mean of the variances of all pairwise variances, (ii) the mean for each factor.combination of the factors specified by LSDby, which is based on the square root of the mean of the variances for all pairwise differences for each factor combination, unless there is only one predction for a factor.combination, when notional LSDs are calculated that are based on the standard error of the prediction multiplied by the square root of two, or (iii) the per.prediction mean, based, for each prediction, on the square root of the mean of the variances for all pairwise differences involving that prediction. It also determines, in conjunction with avsed.tolerance, which LSD will be used in calculating error.intervals and, hence, is used for plots.

LSDby

A character (vector) of variables names, being the names of the factors or numerics in the classify for each combination of which a mean LSD, minLSD and max LSD is stored in the LSD component of the alldiffs.object when meanLSD.type is factor.combinatons.

alpha

The significance level for an LSD to compare a pair of predictions.

…

further arguments passed to allDifferences.data.frame.

Value

An alldiffs.object with components predictions, vcov, differences, p.differences sed, LSD and, if present in alldiffs.obj, backtransforms.

See Also

asremlPlus-package, as.alldiffs, sort.alldiffs, subset.alldiffs, print.alldiffs, renewClassify.alldiffs, redoErrorIntervals.alldiffs, plotPredictions.data.frame, predictPlus.asreml, predictPresent.asreml

Examples

# NOT RUN {
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"))
# }
# 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:Species)
  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:Species", 
                             vcov = TS.vcov, tdf = den.df)
  validAlldiffs(TS.diffs)
}  

## Plot p-values for predictions obtained using asreml or lmerTest
if (exists("TS.diffs"))
{
  ##Recalculate the LSD values for predictions obtained using asreml or lmerTest  
  TS.diffs <- recalcLSD.alldiffs(TS.diffs, meanLSD.type = "factor.combinations", 
                                 LSDby = "Sources")
}
# }