powerLCS: Power analysis for univariate latent change score models

In RAMpath: Structural Equation Modeling Using the Reticular Action Model (RAM) Notation

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)

RAMpath documentation built on Aug. 27, 2023, 5:06 p.m.