powerBLCS {RAMpath}

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, ...)

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

Value

A matrix with power for each parameter.

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: 
powerBLCS(R=1000)

## End(Not run)