sort.predictions.frame {asremlPlus}

Sorts a predictions.frame according to the predicted values associated with a factor.

Description

Sorts the rows of a predictions.frame according to the predicted values in the predictions.frame. These predicted values are generally obtained using predict.asreml by specifying a classify term comprised of one or more variables. Generally, the values associated with one variable are sorted in parallel within each combination of values of the other variables. When there is more than one variable in the classify term, the sorting is controlled using one or more of sortFactor, sortParallelToCombo and sortOrder. If there is only one variable in the classify then the predictions.frame is sorted according to the order of the complete set of predictions.

Usage

## S3 method for class 'predictions.frame'
sort(x, decreasing = FALSE, classify, sortFactor = NULL, 
     sortParallelToCombo = NULL, sortNestingFactor = NULL, 
     sortOrder = NULL, ...)

Arguments

x

A predictions.frame.

decreasing

A logical passed to order that detemines whether the order is for increasing or decreasing magnitude of the predicted values.

classify

A character string giving the variables that define the margins of the multiway table that was predicted. Multiway tables are specified by forming an interaction type term from the classifying variables, that is, separating the variable names with the : operator.

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.frame. It specifies the desired order of the levels in the reordered the predictions.frame. 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)].

...

further arguments passed to or from other methods. Not used at present.

Details

The basic technique is to change the order of the levels of the sortFactor within the predictions.frame so that they are ordered for a subset of predicted values, one for each levels of the sortFactor. When the classify term consists of more than one variable then a subset of one combination of the values of variables other than the sortFactor, the sortParallelToCombo combination, must be chosen for determining the order of the sortFactor levels. Then the sorting of the rows (and columns) will be in parallel within each combination of the values of sortParallelToCombo variables: the classify term, excluding the sortFactor.

Value

The sorted predictions.frame. Also, the sortFactor and sortOrder attributes are set.

Author(s)

Chris Brien

See Also

as.predictions.frame, print.predictions.frame, sort.alldiffs,
predictPlus.asreml, predictPresent.asreml

Examples

##Halve WaterRunoff data to reduce time to execute
data(WaterRunoff.dat)
tmp <- subset(WaterRunoff.dat, Date == "05-18")

##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= tmp)
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"))
  
#Use sort.predictions.frame and save order for use with other response variables
TS.preds <- TS.diffs$predictions
TS.preds.sort <- sort(TS.preds, sortFactor = "Sources", 
                      sortParallelToCombo = list(Type = "Control"))
sort.order <- attr(TS.preds.sort, which = "sortOrder")
  
#Analyse Turbidity
m2.asr <- asreml(fixed = Turbidity ~ Benches + (Sources * (Type + Species)), 
                 random = ~ Benches:MainPlots,
                 keep.order=TRUE, data= tmp)
current.asrt <- as.asrtests(m2.asr)
#Use pH sort.order to sort Turbidity alldiffs object
TS.diffs2 <- predictPlus(m2.asr, classify = "Sources:Type", 
                         pairwise = FALSE, error.intervals = "Stand", 
                         tables = "none", present = c("Type","Species","Sources"))
TS.preds2 <- TS.diffs2$predictions
TS.preds2.sort <- sort(TS.preds, sortFactor = "Sources", sortOder = sort.order)

## 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(tmp))
  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"))
  
  #Use sort.predictions.frame and save order for use with other response variables
  TS.preds.sort <- sort(TS.preds, classify = "Sources:Type", sortFactor = "Sources", 
                        sortParallelToCombo = list(Type = "Control"))
  sort.order <- attr(TS.preds.sort, which = "sortOrder")
  
  #Analyse Turbidity
  m2.lmer <- lmerTest::lmer(Turbidity ~ Benches + (Sources * (Type + Species)) + 
                              (1|Benches:MainPlots),
                            data=na.omit(tmp))
  TS.emm <- emmeans::emmeans(m2.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"))
}