| acc_end_digits {dataquieR} | R Documentation |
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)).
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