renewClassify.alldiffs {asremlPlus}

Renews the components in an alldiffs.object according to a new classify.

Description

The classify is an attribute of an alldiffs.object and determines the order within the components of an unsorted alldiffs.object. This function resets the classify attribute and re-orders the components of alldiffs.object to be in standard order for the variables in a newclassify, using allDifferences.data.frame. The newclassify may be just a re-ordering of the variable names in the previous classify, or be based on a new set of variable names. The latter is particularly useful when linTransform.alldiffs has been used with a matrix and it is desired to replace the resulting Combination classify with a newclassify comprised of a more meaningful set of variables; first replace Combination in the predictions component with the new set of variables and then call renewClassify.

Usage

## S3 method for class 'alldiffs'
renewClassify(alldiffs.obj, newclassify, 
              sortFactor = NULL, sortParallelToCombo = NULL, 
              sortNestingFactor = NULL, sortOrder = NULL, decreasing = FALSE, ...)

Arguments

alldiffs.obj

An alldiffs.object.

newclassify

A character string giving the variables that define the margins of the multiway table that was predicted, but ordered so that the predictions are in the desired order when they are arranged in standard order for the newclassify. Multiway tables are specified by forming an interaction type term from the classifying variables, that is, separating the variable names with the : operator. The number of combined values of the set of variable name(s) must equal the number of rows in the predictions component.

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 factors and numerics, 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 factors and numerics 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 is for increasing or decreasing magnitude of the predicted values.

...

further arguments passed to allDifferences.data.frame; attributes tranform.power, offset and scale cannot be passed.

Details

First, the components of the alldiffs.object is arranged in standard order for the newclassify. Then predictions are reordered according to the settings of sortFactor, sortParallelToCombo, sortOrder and decreasing (see sort.alldiffs for details).

Value

The alldiffs.object supplied with the following components, if present, sorted: predictions, vcov, backtransforms, differences, p.differences and sed. Also, the sortFactor and sortOrder attributes are set.

Author(s)

Chris Brien

See Also

as.alldiffs, allDifferences.data.frame, print.alldiffs, sort.alldiffs,
redoErrorIntervals.alldiffs, recalcLSD.alldiffs,
predictPlus.asreml, predictPresent.asreml

Examples

data(WaterRunoff.dat)

##Use asreml to get predictions and associated statistics

## Not run: 
#Analyse pH  
m1.asr <- asreml(fixed = pH ~ Benches + (Sources * (Type + Species)), 
                 random = ~ Benches:MainPlots,
                 keep.order=TRUE, data= WaterRunoff.dat)
current.asrt <- as.asrtests(m1.asr, NULL, NULL)
current.asrt <- as.asrtests(m1.asr)
current.asrt <- rmboundary(current.asrt)
m1.asr <- current.asrt$asreml.obj

#Get predictions and associated statistics  
TS.diffs <- predictPlus.asreml(classify = "Sources:Type", 
                               asreml.obj = m1.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))
{
  #Analyse pH
  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)
}  

#Re-order predictions from asreml or lmerTest so all Sources for the same Type are together 
#for each combination of A and B
if (exists("TS.diffs"))
{
  TS.diffs.reord <- renewClassify(TS.diffs, newclassify = "Type:Sources")
  validAlldiffs(TS.diffs.reord)
}