A dataset simulated using methods described in the reference below.
Reference: Chow, S., Lu, Z., Sherwood, A., and Zhu, H. (2016). Fitting Nonlinear Ordinary Differential Equation Models with Random Effects and Unknown Initial Conditions Using the Stochastic Approximation Expectation-Maximization (SAEM) Algorithm. Psychometrika, 81(1), 102-134.
A data frame with 10,000 rows and 11 variables
The variables are as follows:
batch. Batch number from simulation
trueInit. True initial condition
id. Person ID
time. Continuous time of measurement
y1. Observed score 1
y2. Observed score 2
y3. Observed score 3
u1. Covariate 1
u2. Covariate 2
trueb. True value of person-specific random effect
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