facCombine.alldiffs {asremlPlus}R Documentation

Combines several factors into one in the components of an alldiffs.object


Combines several factors, in the prediction component of object, into one whose levels are the combinations of the used levels of the individual factors. The matching changes are made to the other components of the alldiffs.object. The levels of the factors are combined using fac.combine from the dae package.


## S3 method for class 'alldiffs'
facCombine(object, factors, order="standard", 
           combine.levels=TRUE, sep="_", level.length = NA, ...)



An alldiffs.object.


A character containing the names of factors in the prediction component of object whose levels are to be combined.


Either standard or yates. The order in which the levels combinations of the factors are to be considered as numbered when forming the levels of the combined factor; standard numbers them as if they are arranged in standard order, that is with the levels of the first factor moving slowest and those of the last factor moving fastest; yates numbers them as if they are arranged in Yates order, that is with the levels of the first factor moving fastest and those of the last factor moving slowest.


A logical specifying whether the levels labels of the new factor are to be combined from those of the factors being combined. The default is to use the integers from 1 to the product of the numbers of combinations of used levels of the individual factors, numbering the levels according to order.


A character string to separate the levels when combine.levels = TRUE.


The maximum number of characters from the the levels of factors to use in the row and column labels of the tables of pairwise differences and their p-values and standard errors.


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


A modified alldiffs.object.


Chris Brien

See Also

as.alldiffs, allDifferences.data.frame, print.alldiffs, sort.alldiffs,
renewClassify.alldiffs; fac.combine in package dae.


  ## 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", 
  HCL.preds <- HCL.pred$pvals
  HCL.sed <- HCL.pred$sed
  HCL.vcov <- NULL
  wald.tab <-  current.asrt$wald.tab
  den.df <- wald.tab[match("Host:Cadavers:Ladybird", rownames(wald.tab)), "denDF"]
## End(Not run)
  ## Use lmeTest and emmmeans to get predictions and associated statistics
  if (requireNamespace("lmerTest", quietly = TRUE) & 
      requireNamespace("emmeans", quietly = TRUE))
    m1.lmer <- lmerTest::lmer(logitP ~ Host*Cadavers*Ladybird + (1|Run),
    HCL.emm <- emmeans::emmeans(m1.lmer, specs = ~ Host:Cadavers:Ladybird)
    HCL.preds <- summary(HCL.emm)
    den.df <- min(HCL.preds$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 <- as.alldiffs(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

    ## Combine Cadavers and Ladybird
    HCL.diffs <- facCombine(HCL.diffs, factors = c("Cadavers","Ladybird"))
    ## Check the validity of HCL.diffs

[Package asremlPlus version 4.2-32 Index]