TrueInit_Y14: Simulated multilevel multi-subject time series of a Van der...
In dynr: Dynamic Models with Regime-Switching
TrueInit_Y14 R Documentation
Simulated multilevel multi-subject time series of a Van der Pol Oscillator
Description
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.
Usage
data(TrueInit_Y14)
Format
A data frame with 60,000 rows and 10 variables
Details
The variables are as follows:
batch. Batch number from simulation
kk. Unclear
trueInit. True initial condition
id. Person ID
time. Continuous time of measurement
y1. Observed score 1
y2. Observed score 2
y3. Observed score 3
co1. Covariate 1
co2. Covariate 2
dynr documentation built on May 29, 2024, 2:49 a.m.
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| TrueInit_Y14 | R Documentation |
Simulated multilevel multi-subject time series of a Van der Pol Oscillator
Description
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.
Usage
data(TrueInit_Y14)
Format
A data frame with 60,000 rows and 10 variables
Details
The variables are as follows:
batch. Batch number from simulation
kk. Unclear
trueInit. True initial condition
id. Person ID
time. Continuous time of measurement
y1. Observed score 1
y2. Observed score 2
y3. Observed score 3
co1. Covariate 1
co2. Covariate 2
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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