acc_end_digits: Extension of acc_shape_or_scale to examine uniform...

In dataquieR: Data Quality in Epidemiological Research

Extension of acc_shape_or_scale to examine uniform distributions of end digits

Description

This implementation contrasts the empirical distribution of a measurement variables against assumed distributions. The approach is adapted from the idea of rootograms (Tukey (1977)) which is also applicable for count data (Kleiber and Zeileis (2016)).

Indicator

Usage

acc_end_digits(
  resp_vars = NULL,
  study_data,
  label_col,
  item_level = "item_level",
  meta_data = item_level,
  meta_data_v2
)

Arguments

resp_vars	variable the names of the measurement variables, mandatory
study_data	data.frame the data frame that contains the measurements
label_col	variable attribute the name of the column in the metadata with labels of variables
item_level	data.frame the data frame that contains metadata attributes of study data
meta_data	data.frame old name for item_level
meta_data_v2	character path to workbook like metadata file, see prep_load_workbook_like_file for details. ALL LOADED DATAFRAMES WILL BE PURGED, using prep_purge_data_frame_cache, if you specify meta_data_v2.

Value

a list with:

• SummaryTable: data.frame with the columns Variables and FLG_acc_ud_shape

• SummaryPlot: ggplot2 distribution plot comparing expected with observed distribution

ALGORITHM OF THIS IMPLEMENTATION:

• This implementation is restricted to data of type float or integer.

• Missing codes are removed from resp_vars (if defined in the metadata)

• The user must specify the column of the metadata containing probability distribution (currently only: normal, uniform, gamma)

• Parameters of each distribution can be estimated from the data or are specified by the user

• A histogram-like plot contrasts the empirical vs. the technical distribution

See Also

Online Documentation

dataquieR documentation built on Jan. 8, 2026, 5:08 p.m.