A data frame with 2000 observations on the following 6 variables. MLGD is a simulated bivariate longitudinal continuous dataset assuming there are 500 subjects in the study whose data are collected at 4 equally-spaced time points.
A data frame with 2000 observations on the following 6 variables.
a numeric vector for subject ID
a numeric vector for the first longitudinal count response
a numeric vector for the second longitudinal count response
a numeric vector for the covariate, X
a numeric vector for the time point at which observations are collected
a numeric vector for the interaction between X and time
The covariates, X and time are the standardized values indeed. The related interaction is calculated by using these standardized values. X is a time-independent covariate. For the details of data generation see the user manual of the R package mmm at http://cran.r-project.org/web/packages/mmm/index.html.
Asar, O. (2012). On multivariate longitudinal binary data models and their applications in forecasting. MS Thesis, Middle East Technical University. Available at http://www.lancaster.ac.uk/pg/asar/thesis-Ozgur-Asar.
Genz, A., Bretz, F., Miwa, T., Mi, X., Leisch, F., Scheipl, F., Hothorn, T. (2011). mvtnorm: Multivariate Normal and t Distributions. R package version 0.9-96. URL http://CRAN.R-project.org/package=mvtnorm.
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