facRecast.alldiffs {asremlPlus}

Reorders and/or revises the factor levels using the order of old levels in levels.order and the new labels for the levels given in newlabels. The values in levels.order must be unique.

Description

Reorders and revises the levels and labels of a factor, in the prediction component of an alldiffs.object. The values in the levels.order vector should be the same as the levels in the existing factor, but the order can be changed. To revise the levels, specify the new levels in the newlabels vector and these will replace the corresponding value in the levels.order vector. The matching changes are made to the other components and attributes of the alldiffs.object.

Usage

## S3 method for class 'alldiffs'
facRecast(object, factor, levels.order = NULL, newlabels = NULL, ...)

Arguments

object

An alldiffs.object.

factor

A character containing the name of a factor in the prediction component of object whose levels and labels are to be recast.

levels.order

A vector of length levels(factor) containing the old levels in the new order for the factor being created. If levels.order is NULL, then the current levels of levels(factor) are used.

newlabels

A vector of length levels(factor) containing values to use in the revision.

...

Further arguments passed to the factor call creating the new factor.

Value

A modified alldiffs.object.

Author(s)

Chris Brien

See Also

as.alldiffs, allDifferences.data.frame, print.alldiffs, sort.alldiffs,
facCombine.alldiffs, facRename.alldiffs, renewClassify.alldiffs; fac.recast in package dae.

Examples

  data("Ladybird.dat")
  
  ## Use asreml to get predictions and associated statistics

  ## Not run: 
  m1.asr <- asreml(logitP ~ Host*Cadavers*Ladybird, 
                   random = ~ Run,
                   data = Ladybird.dat)
  current.asrt <- as.asrtests(m1.asr)
  HCL.pred <- asreml::predict.asreml(m1.asr, classify="Host:Cadavers:Ladybird", 
                                     sed=TRUE)
  HCL.preds <- HCL.pred$pvals
  HCL.sed <- HCL.pred$sed
  HCL.vcov <- NULL
  wald.tab <-  current.asrt$wald.tab
  den.df <- wald.tab[match("Host:Cadavers:Ladybird", rownames(wald.tab)), "denDF"]
  
## End(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(logitP ~ Host*Cadavers*Ladybird + (1|Run),
                              data=Ladybird.dat)
    HCL.emm <- emmeans::emmeans(m1.lmer, specs = ~ Host:Cadavers:Ladybird)
    HCL.preds <- summary(HCL.emm)
    den.df <- min(HCL.preds$df)
    ## Modify HCL.preds to be compatible with a predictions.frame
    HCL.preds <- as.predictions.frame(HCL.preds, predictions = "emmean", 
                                      se = "SE", interval.type = "CI", 
                                      interval.names = c("lower.CL", "upper.CL"))
    HCL.vcov <- vcov(HCL.emm)
    HCL.sed <- NULL
  }
  
  ## Use the predictions obtained with either asreml or lmerTest
  if (exists("HCL.preds"))
  {
    ## Form an all.diffs object
    HCL.diffs <- allDifferences(predictions = HCL.preds, classify = "Host:Cadavers:Ladybird", 
                                sed = HCL.sed, vcov = HCL.vcov, tdf = den.df)
    
    ## Check the class and validity of the alldiffs object
    is.alldiffs(HCL.diffs)
    validAlldiffs(HCL.diffs)

    ## Recast the Ladybird and Host factors
    HCL.diffs <- facRecast(HCL.diffs, factor = "Ladybird", 
                           newlabels = c("none", "present"))
    HCL.diffs <- facRecast(HCL.diffs, factor = "Ladybird", 
                           levels.order = c("present", "none"), 
                           newlabels = c("yes","no"))
    HCL.diffs <- facRecast.alldiffs(HCL.diffs, factor = "Host", 
                                    levels.order = c("trefoil", "bean"))

    ## Check the validity of HCL.diffs
    validAlldiffs(HCL.diffs)
  }