This function builds a simulated dataset and classifies individuals based on their change scores to see whether we can recover the changers from the true DGP.
Usage
detectChangers(
n = 1000,
t = 3,
rate = 0.5,
balance_dir = 0.5,
balance_res = 0.5,
strength = 0.5,
reliable = 1,
n_samples = 1000,
verbose = TRUE
)Arguments
- n
The number of individuals in the DGP.
- t
The number of time periods.
- rate
The percent of individuals changing across the panel.
- balance_dir
The direction of change, where 0 means all negative change, 1 means all positive change, and values in-between means the percentage of changers changing positively.
- balance_res
The marginal distribution of the outcome (the percent distribution of 0s and 1).
- strength
The strength of change in the latent variable.
- reliable
The reliability score of the outcome measurement.
- n_samples
The number of simulated datasets.
- verbose
See progress messages in the console.