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.
|