asremlPlus (version 4.2-26)

redoErrorIntervals.alldiffs: Adds or replaces the error intervals stored in a prediction component of an alldiffs.object.

Description

Given an alldiffs.object, adds or replaces error.intervals for its prediction component. If the backtransforms component is present, the transform.power, offset and scale will be retieved from the backtransforms attributes, ignoring the values for the function's arguments, and the backtransformed error.intervals will also be calculated.

Usage

# S3 method for alldiffs
redoErrorIntervals(alldiffs.obj, error.intervals = "Confidence", 
                   alpha = 0.05, avsed.tolerance = 0.25, 
                   meanLSD.type = NULL, LSDby = NULL, ...)

Arguments

alldiffs.obj

An alldiffs.object.

error.intervals

A character string indicating the type of error interval, if any, to calculate in order to indicate uncertainty in the predicted values. Possible values are "none", "StandardError", "Confidence" and "halfLeastSignificant". The default is for confidence limits to be used. The "halfLeastSignificant" option results in half the Least Significant Difference (LSD) being added and subtracted to the predictions, the LSD being calculated using the square root of the mean of the variances of all or a subset of pairwise differences between the predictions. If the LSD is zero, as can happen when predictions are constrained to be equal, then the limits of the error intervals are set to NA. If meanLSD.type is set to overall, the avsed.tolerance is not NA and the range of the SEDs divided by the average of the SEDs exceeds avsed.tolerance then the error.intervals calculations and the plotting will revert to confidence intervals.

alpha

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

avsed.tolerance

A numeric giving the value of the SED range, the range of the SEDs divided by the square root of the mean of the variances of all or a subset of the pairwise differences, that is considered reasonable in calculating error.intervals. It should be a value between 0 and 1. The following rules apply:

1. If avsed.tolerance is NA then mean LSDs of the type specified by meanLSD.type are calculated and used in error.intervals and plots.

2. Irrespective of the setting of meanLSD.type, if avsed.tolerance is not exceeded then the mean LSDs are used in error.intervals and plots.

3. If meanLSD.type is set to overall, avsed.tolerance is not NA, and avsed.tolerance is exceeded then error.intervals and plotting revert to confidence intervals.

4. If meanLSD.type is set to factor.combinations and avsed.tolerance is not exceeded for any factor combination then the half LSDs are used in error.intervals and plots; otherwise, error.intervals and plotting revert to confidence intervals.

5. If meanLSD.type is set to per.prediction and avsed.tolerance is not exceeded for any prediction then the half LSDs are used in error.intervals and plots; otherwise, error.intervals and plotting revert to confidence intervals.

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, 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. If NULL, the attributes meanLSD.type and LSDby of the alldiffs.obj will be used to determine the LSDs to be calculated. If the meanLSD.type attribute is NULL then meanLSD.type will be set to overall. The meanLSD.type also determines, in conjunction with avsed.tolerance, which LSD will be usedin 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.

…

pass transform.power, offset and scale as required - for further information see predictPlus.asreml.

Value

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

If error.intervals is not "none", then the predictions component and, if present, the backtransforms component will contain columns for the lower and upper values of the limits for the interval. The names of these columns will consist of three parts separated by full stops: 1) the first part will be lower or upper; 2) the second part will be one of Confidence, StandardError or halfLeastSignificant; 3) the third component will be limits.

The name of the response, the term, the classify and tdf, as well as the degrees of freedom of the standard error, will be set as attributes to the object.

See Also

as.alldiffs, print.alldiffs, sort.alldiffs, subset.alldiffs, allDifferences.data.frame, recalcLSD.alldiffs, predictPresent.asreml, plotPredictions.data.frame, as.Date, predict.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 <- redoErrorIntervals.alldiffs(TS.diffs, 
                                          error.intervals = "halfLeastSignificant")

}
# }