Impute missing values — impute

Impute missing values in items of a data frame based on their dic attributes. You can specify a filter to select which items to impute based on their dic attributes. If force_to_scale is set to TRUE, the imputed values will be rounded and constrained to the scale's minimum and maximum values.

Usage

impute_missing(
  data,
  filter = NULL,
  method = "continuous",
  force_to_scale = TRUE
)

Arguments

data: A data frame.
filter: A logical expression for any dic attribute (e.g. scale == "ITRF" & subscale == "Int").
method: Method for imputation. Either "continuous" (default) or "ordinal". If "continuous", the Amelia package will be used for imputation. If "ordinal", the mice package will be used for imputation with ordinal logistic regression.
force_to_scale: If TRUE, imputed values will be rounded and forced to the scale. That is, a value below the scale's minimum or maximum will be set to the scale's minimum and maximum. If FALSE, imputed values will not be adjusted.

Value

A data frame with imputed data.

Details

This function uses the Amelia (continuous) or mice (ordinal) package to impute missing values in items of a data frame. You can specify a filter to select which items to impute based on their dic attributes. If force_to_scale is set to TRUE, the imputed values will be rounded and constrained to the scale's minimum and maximum values.