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,
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 |
meta_data_v2 |
character path to workbook like metadata file, see
|
Value
a list with:
-
SummaryTable
: data.frame with the columnsVariables
andFLG_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