acc_shape_or_scale: Compare observed versus expected distributions

In dataquieR: Data Quality in Epidemiological Research

Compare observed versus expected distributions

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).

Usage

acc_shape_or_scale(
  resp_vars,
  dist_col,
  guess,
  par1,
  par2,
  end_digits,
  label_col,
  study_data,
  meta_data,
  flip_mode = "noflip"
)

Arguments

resp_vars	variable the name of the continuous measurement variable
dist_col	variable attribute the name of the variable attribute in meta_data that provides the expected distribution of a study variable
guess	logical estimate parameters
par1	numeric first parameter of the distribution if applicable
par2	numeric second parameter of the distribution if applicable
end_digits	logical internal use. check for end digits preferences
label_col	variable attribute the name of the column in the metadata with labels of variables
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
flip_mode	enum default | flip | noflip | auto. Should the plot be in default orientation, flipped, not flipped or auto-flipped. Not all options are always supported. In general, this con be controlled by setting the roptions(dataquieR.flip_mode = ...). If called from dq_report, you can also pass flip_mode to all function calls or set them specifically using specific_args.

Value

a list with:

• SummaryData: data.frame underlying the plot

• SummaryPlot: ggplot2 probability distribution plot

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

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 July 26, 2023, 6:10 p.m.