outlier.replace function

Outlier detection and substitution

Starting by a previously estimated averaging model, this function detect outliers according to a Bonferroni method. The outliers can be substituted with a user-defined value.

outlier.replace(object, whichModel = NULL, alpha = 0.05, value = NA)

Arguments

• object: An object of class 'rav', containing the estimated averaging models.

• whichModel: Argument that specifies which of the predicted models has to be compared to the observed data. Options are:

  1. "null": null model
  2. "ESM": equal scale values model
  3. "SAM": simple averaging model
  4. "EAM": equal-weights averaging model
  5. "DAM": differential-weight averaging model
  6. "IC": information criteria

  As default setting, the (first) best model is used.

• alpha: Critical value for the z-test on residuals.

• value: Argument that can be used to set a replacement for the outliers. If a function is specified, it is applied to each column of the final matrix: the resulting value is used to replace outliers detected on the same column.

Returns

A data object in which outliers have been removed or replaced.

See Also

rav, rAverage-package,

Examples

data(pasta)
model <- rav(pasta, subset="s04", lev=c(3,3), names=c("Price","Packaging"))
outlier.replace(model, value=mean)
outlier.replace(model, whichModel="IC", value=NA)