powerLCS: Power analysis for univariate latent change score models

Description Usage Arguments Value References Examples

Description

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

Usage

1
2
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. (2016). Sample Size Planning for Latent Change Score Models through Monte Carlo Simulation.

Examples

1
2
3
4
## Not run: 
powerLCS(R=1000)

## End(Not run)

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