VARsim {dynr}R Documentation

Simulated time series data for multiple imputation in dynamic modeling.

Description

A dataset simulated using a vector autoregressive (VAR) model of order 1 with two observed variables and two covariates. Data are generated following the simulation design illustrated by Ji and colleagues (2018). Specifically, missing data are generated following the missing at random (MAR) condition under which the probability of missingness in both dependent variables and covariates is conditioned on two completely observed auxiliary variables.

Usage

data(VARsim)

Format

A data frame with 10000 rows and 8 variables

Details

The variables are as follows:

References

Ji, L., Chow, S-M., Schermerhorn, A.C., Jacobson, N.C., & Cummings, E.M. (2018). Handling Missing Data in the Modeling of Intensive Longitudinal Data. Structural Equation Modeling: A Multidisciplinary Journal, 1-22.


[Package dynr version 0.1.16-27 Index]