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.
A data frame with 10000 rows and 8 variables
The variables are as follows:
ID. ID of the participant (1 to 100)
Time. Time index (100 time points from each subject)
ca. Covariate 1
cn. Covariate 2
wp. Dependent variable 1
hp. Dependent variable 2
x1. Auxiliary variable 1
x2. Auxiliary variable 2
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.
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