powerBLCS: Power analysis for bivariate latent change score models

Description Usage Arguments Value References Examples

Description

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

Usage

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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. (2016). Sample Size Planning for Latent Change Score Models through Monte Carlo Simulation.

Examples

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## Not run: 
powerBLCS(R=1000)

## End(Not run)

RAMpath documentation built on May 2, 2019, 9:12 a.m.