Description Usage Format Details References Examples

A data frame with 2000 observations on the following 6 variables. MLCD is a simulated bivariate longitudinal count dataset assuming there are 500 subjects in the study whose data are collected at 4 equally-spaced time points.

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A data frame with 2000 observations on the following 6 variables.

`ID`

a numeric vector for subject ID

`resp1`

a numeric vector for the first longitudinal count response

`resp2`

a numeric vector for the second longitudinal count response

`X`

a numeric vector for the covariate, X

`time`

a numeric vector for the time point at which observations are collected

`X.time`

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.

Erhardt, V. (2009). corcounts: Generate Correlated Count Random Variable. R package version 1.4. URL http://CRAN.R-project.org/package=corcounts.

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Loading required package: gee
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