validate_datalong: Validate a longdata object

In rbmi: Reference Based Multiple Imputation

Validate a longdata object

Description

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 by set_vars()
data_ice	a data.frame containing the subjects ICE data. See draws() 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 the data

• 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. that nrow(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.

rbmi documentation built on Nov. 24, 2023, 5:11 p.m.