acc_varcomp: Utility function to compute model-based ICC depending on the...

In dataquieR: Data Quality in Epidemiological Research

Utility function to compute model-based ICC depending on the (statistical) data type

Description

This function is still under construction. It is designed to run for any statistical data type as follows:

• Variables with only two distinct values will be modeled by mixed effects logistic regression.

• Nominal variables will be transformed to binary variables. This can be user-specified using the metadata columns RECODE_CASES and/or RECODE_CONTROL. Otherwise, the most frequent category will be assigned to cases and the remaining categories to control. As for other binary variables, the ICC will be computed using a mixed effects logistic regression.

• Ordinal variables will be analyzed by linear mixed effects models, if every level of the variable has at least as many observations as specified in the argument cut_off_linear_model_for_ord. Otherwise, the data will be modeled by a mixed effects ordered regression, if the package ordinal is available.

• Metric variables with integer values are analyzed by linear mixed effects models.

• For variables with data type float, the existing implementation acc_varcomp is called, which also uses linear mixed effects models.

Indicator

Usage

acc_varcomp(
  resp_vars = NULL,
  group_vars = NULL,
  co_vars = NULL,
  study_data,
  label_col,
  item_level = "item_level",
  min_obs_in_subgroup = 10,
  min_subgroups = 5,
  cut_off_linear_model_for_ord = 10,
  threshold_value = lifecycle::deprecated(),
  meta_data = item_level,
  meta_data_v2
)

Arguments

resp_vars	variable the name of the measurement variable
group_vars	variable the name of the examiner, device or reader variable
co_vars	variable list a vector of covariables, e.g. age and sex, for adjustment
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
min_obs_in_subgroup	integer from=0. This optional argument specifies the minimum number of observations that is required to include a subgroup (level) of the group_var in the analysis. Subgroups with less observations are excluded.
min_subgroups	integer from=0. This optional argument specifies the minimum number of subgroups (level) of the group_var that is required to run the analysis. If there are less subgroups, the analysis is not conducted.
cut_off_linear_model_for_ord	integer from=0. This optional argument specifies the minimum number of observations for individual levels of an ordinal outcome (resp_var) that is required to run a linear mixed effects model instead of a mixed effects ordered regression (i.e., a cut-off value above which linear models are considered a good approximation). The argument can be set to NULL if ordered regression models are preferred for ordinal data in any case.
threshold_value	Deprecated.
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.

Details

Not yet described

Value

The function returns two data frames, 'SummaryTable' and 'SummaryData', that differ only in the names of the columns.

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