powerBLCS: Power analysis for bivariate latent change score models

Description

Calculate power for bivariate latent change score models based on Monte Carlo simulation.

Usage

powerBLCS(N=100, T=5, R=1000, betay=0, my0=0, mys=0, varey=1, 
vary0=1, varys=1, vary0ys=0, alpha=0.05, betax=0, mx0=0, 
mxs=0, varex=1, varx0=1, varxs=1, varx0xs=0, varx0y0=0,  
varx0ys=0, vary0xs=0, varxsys=0, gammax=0, gammay=0, ...)

Value

A matrix with power for each parameter.

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
betax: Population parameter values
mx0: Population parameter values
mxs: Population parameter values
varex: Population parameter values
varx0: Population parameter values
varxs: Population parameter values
varx0xs: Population parameter values
gammax: Population parameter values
gammay: Population parameter values
varx0y0: Population parameter values
varx0ys: Population parameter values
vary0xs: Population parameter values
varxsys: Population parameter values
alpha: Significance level
...: Options can be used for lavaan

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

Run this code

if (FALSE) {
powerBLCS(R=1000)
}

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