common_arguments: arguments

In sdcLog: Tools for Statistical Disclosure Control in Research Data Centers

arguments

Description

arguments

Arguments

data	data.frame from which the descriptive statistics are calculated.
id_var	character The name of the id variable. Defaults to getOption("sdc.id_var") so that you can provide options(sdc.id_var = "my_id_var") at the top of your script.
val_var	character vector of value variables on which descriptive statistics are computed.
by	character vector of grouping variables.
zero_as_NA	logical If TRUE, zeros in 'val_var' are treated as NA.
fill_id_var	logical Only for very specific use cases. For example: id_var contains NA values which represent missing values in the sense that there actually exist values identifying the entity but are unknown (or deleted for privacy reasons). id_var contains NA values which result from the fact that an observation features more than one confidential identifier and not all of these identifiers are present in each observation. Examples for such identifiers are the role of a broker in a security transaction or the role of a collateral giver in a credit relationship. If TRUE, NA values within id_var will internally be filled with <filled_[i]>, assuming that all NA values of id_var can be treated as different small entities for statistical disclosure control purposes. Thus, set TRUE only if this is a reasonable assumption. Defaults to FALSE.
model	The estimated model object. Can be a model type like lm, glm and various others (anything which can be handled by broom::augment()).
min_obs	integer The minimum number of observations used to calculate the minimum and maximum. Defaults to getOption("sdc.n_ids", 5L). This is not the number of distinct entities.
max_obs	integer The maximum number of observations used to calculate the minimum and maximum. Defaults to nrow(data). This is not the number of distinct entities.

sdcLog documentation built on March 20, 2022, 1:06 a.m.