| powerLCS {RAMpath} | R Documentation |
Power analysis for univariate latent change score models
Description
Calculate power for univariate latent change score models based on Monte Carlo simulation.
Usage
powerLCS(N=100, T=5, R=1000, betay=0, my0=0, mys=0,
varey=1, vary0=1, varys=1, vary0ys=0, alpha=0.05, ...)
Arguments
N |
Sample size, can be a scalar or a vector. For better performance, make sure N is at least two times of T |
T |
Number of times, occasions or waves of measurements, can be a scalar or a vector |
R |
Number of replications to run in Monte Carlo simulation. Recommended 1000 or more |
betay |
Population parameter values |
my0 |
Population parameter values |
mys |
Population parameter values |
varey |
Population parameter values |
vary0 |
Population parameter values |
varys |
Population parameter values |
vary0ys |
Population parameter values |
alpha |
Significance level |
... |
Options can be used for lavaan |
Value
model |
The lavaan model specification of the bivariate latent change score model |
lavaan |
The lavaan output |
ram |
Output in terms of RAM matrices |
References
Zhang, Z., & Liu, H. (2018). Sample size and measurement occasion planning for latent change score models through Monte Carlo simulation. In E. Ferrer, S. M. Boker, and K. J. Grimm (Eds.), Advances in longitudinal models for multivariate psychology: A festschrift for Jack McArdle (pp. 189-211). New York, NY: Routledge.
Examples
## Not run:
powerLCS(R=1000)
## End(Not run)