# powerBLCS: Power analysis for bivariate latent change score models In RAMpath: Structural Equation Modeling Using the Reticular Action Model (RAM) Notation

## Description

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

## Usage

 ```1 2 3 4``` ```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

 ```1 2 3 4``` ```## Not run: powerBLCS(R=1000) ## End(Not run) ```

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