Validate a longdata object
Usage
validate_datalong(data, vars)
validate_datalong_varExists(data, vars)
validate_datalong_types(data, vars)
validate_datalong_notMissing(data, vars)
validate_datalong_complete(data, vars)
validate_datalong_unifromStrata(data, vars)
validate_dataice(data, data_ice, vars, update = FALSE)
Arguments
- data
a
data.frame
containing the longitudinal outcome data + covariates for multiple subjects- vars
a
vars
object as created byset_vars()
- data_ice
a
data.frame
containing the subjects ICE data. Seedraws()
for details.- update
logical, indicates if the ICE data is being set for the first time or if an update is being applied
Details
These functions are used to validate various different parts of the longdata object
to be used in draws()
, impute()
, analyse()
and pool()
. In particular:
validate_datalong_varExists - Checks that each variable listed in
vars
actually exists in thedata
validate_datalong_types - Checks that the types of each key variable is as expected i.e. that visit is a factor variable
validate_datalong_notMissing - Checks that none of the key variables (except the outcome variable) contain any missing values
validate_datalong_complete - Checks that
data
is complete i.e. there is 1 row for each subject * visit combination. e.g. thatnrow(data) == length(unique(subjects)) * length(unique(visits))
validate_datalong_unifromStrata - Checks to make sure that any variables listed as stratification variables do not vary over time. e.g. that subjects don't switch between stratification groups.