as.alldiffs: Forms an `alldiffs.object` from the supplied predictions, along with those statistics, associated with the predictions and their pairwise differences, that have been supplied.

Description

Creates an alldiffs.object that consists of a list containing the following components: predictions, vcov, differences, p.differences, sed, LSD and backtransforms. Predictions must be supplied to the function while the others will be set only if they are supplied; those not supplied are set to NULL. It also has attributes response, response.title, term, classify, tdf, tdf, alpha, sortFactor and sortOrder. which will be set to the values supplied or NULL if none are supplied.

Usage

as.alldiffs(predictions, vcov = NULL, differences = NULL, 
            p.differences = NULL, sed = NULL, LSD = NULL, 
            backtransforms = NULL, 
            response = NULL, response.title = NULL, 
            term = NULL, classify = NULL, 
            tdf = NULL, alpha = 0.05, 
            sortFactor = NULL, sortOrder = NULL)

Value

An S3-class alldiffs.object. Also, see predictPlus.asreml for more information.

Arguments

predictions

A predictions.frame, being a data.frame beginning with the variables classifying the predictions and also containing columns named predicted.value, standard.error and est.status; each row contains a single predicted value. It may also contain columns for the lower and upper limits of error intervals for the predictions. Note that the names standard.error and est.status have been changed to std.error and status in the pvals component produced by asreml-R4; if the new names are in the data.frame supplied to predictions, they will be returned to the previous names.

differences

A matrix containing all pairwise differences between the predictions; it should have the same number of rows and columns as there are rows in predictions.

p.differences

A matrix containing p-values for all pairwise differences between the predictions; each p-value is computed as the probability of a t-statistic as large as or larger than the observed difference divided by its standard error. The degrees of freedom of the t distribution for computing it are computed as the denominator degrees of freedom of the F value for the fixed term, if available; otherwise, the degrees of freedom stored in the attribute tdf are used; the matrix should be of the same size as that for differences.

sed

A matrix containing the standard errors of all pairwise differences between the predictions; they are used in computing the p-values.

vcov

A matrix containing the variance matrix of the predictions; it is used in computing the variance of linear transformations of the predictions.

LSD

An LSD.frame containing the mean, minimum and maximum LSD for determining the significance of pairwise differences, as well as an assigned LSD and a measure of the accuracy of the LSD. If LSD is NULL then the LSD.frame stored in the LSD component will be calculated and the values of LSDtype, LSDby and LSDstatistic added as attributes of the alldiffs.object. The LSD for a single prediction assumes that any predictions to be compared are independent; this is not the case if residual errors are correlated.

backtransforms

A data.frame containing the backtransformed values of the predicted values that is consistent with the predictions component, except that the column named predicted.value is replaced by one called backtransformed.predictions. Any error.interval values will also be the backtransformed values. Each row contains a single predicted value.

response

A character specifying the response variable for the predictions. It is stored as an attribute to the alldiffs.object.

response.title

A character specifying the title for the response variable for the predictions. It is stored as an attribute to the alldiffs.object.

term

A character string giving the variables that define the term that was fitted using asreml and that corresponds to classify. It only needs to be specified when it is different to classify; it is stored as an attribute of the alldiffs.object. It is likely to be needed when the fitted model includes terms that involve both a numeric covariate and a factor that parallel each other; the classify would include the covariate and the term would include the factor.

classify

A character string giving the variables that define the margins of the multiway table used in the prediction. Multiway tables are specified by forming an interaction type term from the classifying variables, that is, separating the variable names with the : operator. It is stored as an attribute to the alldiffs.object.

tdf

an integer specifying the degrees of freedom of the standard error. It is used as the degrees of freedom for the t-distribution on which p-values and confidence intervals are based. It is stored as an attribute to the alldiffs.object.

alpha

A numeric giving the significance level for LSDs or one minus the confidence level for confidence intervals. It is stored as an attribute to the alldiffs.object.

sortFactor

A character containing the name of the factor that indexes the set of predicted values that determined the sorting of the components.

sortOrder

A character vector that is the same length as the number of levels for sortFactor in the predictions component of the alldiffs.object. It specifies the order of the levels in the reordered components of the alldiffs.object.

The following creates a sortOrder vector levs for factor f based on the values in x: levs <- levels(f)[order(x)].

Author

Chris Brien

Examples

Run this code

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

  if (FALSE) {
  m1.asr <- asreml(Yield ~ Nitrogen*Variety, 
                   random=~Blocks/Wplots,
                   data=Oats.dat)
  current.asrt <- as.asrtests(m1.asr)
  Var.pred <- asreml::predict.asreml(m1.asr, classify="Nitrogen:Variety", 
                                      sed=TRUE)
  if (getASRemlVersionLoaded(nchar = 1) == "3")
    Var.pred <- Var.pred$predictions
  Var.preds <- Var.pred$pvals
  Var.sed <- Var.pred$sed
  Var.vcov <- NULL
  }
  
  ## Use lmerTest and emmmeans to get predictions and associated statistics
  if (requireNamespace("lmerTest", quietly = TRUE) & 
      requireNamespace("emmeans", quietly = TRUE))
  {
    m1.lmer <- lmerTest::lmer(Yield ~ Nitrogen*Variety + (1|Blocks/Wplots),
                              data=Oats.dat)
    Var.emm <- emmeans::emmeans(m1.lmer, specs = ~ Nitrogen:Variety)
    Var.preds <- summary(Var.emm)
    den.df <- min(Var.preds$df)
    ## Modify Var.preds to be compatible with a predictions.frame
    Var.preds <- as.predictions.frame(Var.preds, predictions = "emmean", 
                                      se = "SE", interval.type = "CI", 
                                      interval.names = c("lower.CL", "upper.CL"))
    Var.vcov <- vcov(Var.emm)
    Var.sed <- NULL
  }

  ## Use the predictions obtained with either asreml or lmerTest
  if (exists("Var.preds"))
  {
    ## Form an all.diffs object
     Var.diffs <- as.alldiffs(predictions = Var.preds, classify = "Nitrogen:Variety", 
                              sed = Var.sed, vcov = Var.vcov, tdf = den.df)

    ## Check the class and validity of the alldiffs object
    is.alldiffs(Var.diffs)
    validAlldiffs(Var.diffs)
  }