This is a simulated dataset for illustration. It contains a total of \(N=436\) observations at irregularly spaced time points for \(n=50\) subjects. There are \(p=100\) covariates.
data(SimulatedData)This simulated dataset contains \(N=436\) observations for \(n=50\) subjects, with \(p=100\) covariates. The first column y gives the response variables, the second column t gives the observation times, the third column id gives the unique IDs for each of the 50 subjects, and columns 4-103 (x1, ..., x100) give the covariate values.
This synthetic dataset is a slight modification from Experiment 2 in Section 5.1 of Bai et al. (2023). We use \(p=100\) for illustration, instead of \(p=500\) as in the paper.
Bai, R., Boland, M. R., and Chen, Y. (2023). "Scalable high-dimensional Bayesian varying coefficient models with unknown within-subject covariance." Journal of Machine Learning Research, 24:1-49.