VARsim: Simulated time series data for multiple imputation in dynamic...
In dynr: Dynamic Models with Regime-Switching
VARsim 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:
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
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.
dynr documentation built on Oct. 17, 2022, 9:06 a.m.
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| VARsim | 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:
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
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.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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