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, meta_data, label_col = VAR_NAMES)

Arguments

resp_vars	variable the names of the measurement variables, mandatory
study_data	data.frame the data frame that contains the measurements
meta_data	data.frame the data frame that contains metadata attributes of study data
label_col	variable attribute the name of the column in the metadata with labels of variables

Value

a list with:

• SummaryTable: data frame underlying the plot

• 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 May 29, 2024, 7:18 a.m.