mmm (version 1.4)

multiLongGaussian: Multivariate Longitudinal Continuous (Gaussian) Data

Description

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

Usage

data(multiLongGaussian)

Format

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

Details

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. The R script to generate the data set is given in the Examples section of the mmm function.

References

Asar, O. (2012). On multivariate longitudinal binary data models and their applications in forecasting. MS Thesis, Middle East Technical University

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.

Examples

data(multiLongGaussian)
plot(multiLongGaussian$X,multiLongGaussian$resp1)