For the purposes of the package examples, the data set was adapted from the numerical simulations of the original manuscript. Specifically, data was generated for 400 subjects. The number of observation times for the response was Poisson distributed with intensity rate 5, and similarly for the number of observation times for the covariates. Observation times are generated from a uniform distribution Unif(0,1) independently. The covariate process is Gaussian, with values at fixed time points being multivariate normal with mean 0, variance 1 and correlation exp(-|t_ij - t_ik|). The responses were generated from Y(t) = beta0 + X(t)*beta1 + epsilon(t), where beta0 = 0.5, beta1 = 1.5, and epsilon(t) is Gaussian with mean 0, variance 1 and cov(e(s),e(t)) = 2^-|t-s|. Covariates are stored as TI.x. Responses are stored as TI.y.
For the purposes of the package examples, the data set was adapted from the numerical simulations of the original manuscript. Specifically, data was generated for 400 subjects. The number of observation times for the response was Poisson distributed with intensity rate 5, and similarly for the number of observation times for the covariates. Observation times are generated from a uniform distribution Unif(0,1) independently. The covariate process is Gaussian, with values at fixed time points being multivariate normal with mean 0, variance 1 and correlation exp(-|t_ij - t_ik|). The responses were generated from Y(t) = beta0 + X(t)*beta1 + epsilon(t), where beta0 = 0.5, beta1 = 0.4t + 0.5, and epsilon(t) is Gaussian with mean 0, variance 1 and cov(e(s),e(t)) = 2^-|t-s|. Covariates are stored as TD.x. Responses are stored as TD.y.
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