asremlPlus (version 4.2-32)

sort.alldiffs: Sorts the components in an alldiffs.object according to the predicted values associated with a factor.

Description

Sorts the rows of the components in an alldiffs.object (see as.alldiffs) that are data.frames and the rows and columns of those that are matrices according to the predicted values in the predictions component. 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, sortWithinVals and sortOrder. If there is only one variable in the classify then all components are sorted according to the order of the complete set of predictions.

Note that reordering the classify variables in the alldiffs.object and changing the order of the rows and columns of the components so that they are in standard order for the new variable order can be achieved using either renewClassify.alldiffs or allDifferences.data.frame.

Usage

# S3 method for alldiffs
sort(x, decreasing = FALSE, classify = NULL, 
     sortFactor = NULL, sortWithinVals = NULL, sortOrder = NULL, ...)

Arguments

x

An alldiffs.object.

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. If NULL, it will be obtained from the classify attribute of the as.alldiffs object supplied through x.

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 sortWithin 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 sortWithin variables. The order to use is determined by either sortWithinVals or sortOrder.

sortWithinVals

A list with a component named for each factor and numeric that is a classify variable for the predictions, excluding sortFactor. Each component should contain a single value that is a value of the variable. 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 to be used for all combinations of the sortWithinVals variables. If sortWithinVals is NULL then the first value of each sortWithin variable in predictions component is used to define sortWithinVals. If there is only one variable in the classify then sortWithinVals 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 sortWithinVals 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.

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.

Details

The basic technique is to change the order of the levels of the sortFactor within the predictions and, if present, backtransforms components 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 sortWithin set, 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 sortWithin variables: the classify term, excluding the sortFactor.

See Also

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

Examples

# NOT RUN {
##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.alldiffs and save order for use with other response variables
TS.diffs.sort <- sort(diffs, sortFactor = "Sources", sortWithinVals = list(Type = "Control"))
sort.order <- attr(TS.diffs.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
diffs2.sort <- predictPlus(m2.asr, classify = "Sources:Type", 
                           pairwise = FALSE, error.intervals = "Stand", 
                           tables = "none", present = c("Type","Species","Sources"),
                           sortFactor = "Sources", 
                           sortOrder = sort.order)
# }
# 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"))
  
  ## 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)
    
  #Use sort.alldiffs and save order for use with other response variables
  TS.diffs.sort <- sort(TS.diffs, sortFactor = "Sources", 
                        sortWithinVals = list(Type = "Control"))
  sort.order <- attr(TS.diffs.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"))
    
  ## Form an all.diffs object, sorting it using the pH sort.order and check its validity
  TS.vcov <- vcov(TS.emm)
  TS.diffs2.sort <- allDifferences(predictions = TS.preds, 
                                   classify = "Sources:Type", 
                                   vcov = TS.vcov, tdf = den.df,
                                   sortFactor = "Sources", 
                                   sortOrder = sort.order)
  validAlldiffs(TS.diffs2.sort)
}  
# }