Description Usage Arguments Value References Examples

A power curve is useful to graphically display how power changes with sample size (e.g., Zhang & Wang). This function is to generate a power curve for SEM based on Monte Carlo simulation, either using Sobel test or bootstrap method to test the indirect / mediation effects if applicable.

1 2 3 4 5 |

`model` |
Model specified using lavaan syntax. More about model specification can be found in Rosseel (2012). |

`indirect` |
Indirect effect difined using lavaan syntax. |

`nobs` |
Sample size. It is 100 by default. |

`type` |
The method used to test the indirect effects ( |

`nrep` |
Number of replications for the Monte Carlo simulation. It is 1000 by default. |

`nboot` |
Number of replications for the bootstrap to test the specified parameter (e.g., mediation). It is 1000 by default. |

`alpha` |
significance level chosed for the test. It equals 0.05 by default. |

`skewness` |
A sequence of skewnesses of the observed variables. It is not required. |

`kurtosis` |
A sequence of kurtosises of the observed variables. It is not required. |

`ovnames` |
Names of the observed variables in the model. It is not required. |

`se` |
The method for calculatating the standard errors. Its default method "default" is regular standard errors. More about methods specification standard errors calculatationcan be found in Rosseel (2012). |

`estimator` |
Estimator. It is Maxmum likelihood estimator by default. More about estimator specification can be found in Rosseel (2012). |

`parallel` |
Parallel computing ( |

`ncore` |
Number of processors used for parallel computing. By default, ncore = Sys.getenv('NUMBER_OF_PROCESSORS'). |

`cl` |
Number of clusters. It is NULL by default. When it is NULL, the program will detect the number of clusters automatically. |

`...` |
Extra arguments. It is not required. |

An object of the power analysis. The power for all parameters in the model as well as the indirect effects if specified.

Rosseel, Y. (2012). Lavaan: An R package for structural equation modeling and more. Version 0.5<e2><80><93>12 (BETA). Ghent, Belgium: Ghent University.

Thoemmes, F., MacKinnon, D. P., & Reiser, M. R. (2010). Power analysis for complex mediational designs using Monte Carlo methods. Structural Equation Modeling, 17(3), 510-534.

Zhang, Z., & Yuan, K.-H. (2018). Practical Statistical Power Analysis Using Webpower and R (Eds). Granger, IN: ISDSA Press.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 | ```
## Not run:
#To specify the model
ex2model ="
ept ~ start(0.4)*hvltt + b*hvltt + start(0)*age + start(0)*edu + start(2)*R
hvltt ~ start(-0.35)*age + a*age +c*edu + start(0.5)*edu
R ~ start(-0.06)*age + start(0.2)*edu
R =~ 1*ws + start(0.8)*ls + start(0.5)*lt
age ~~ start(30)*age
edu ~~ start(8)*edu
age ~~ start(-2.8)*edu
hvltt ~~ start(23)*hvltt
R ~~ start(14)*R
ws ~~ start(3)*ws
ls ~~ start(3)*ls
lt ~~ start(3)*lt
ept ~~ start(3)*ept
"
#To specify the indirect effects
indirect = "ind1 := a*b + c*b"
nobs <- seq(100, 2000, by =200)
#To calculate power curve:
power.curve = wp.mc.sem.power.curve(model=ex2model, indirect=indirect,
nobs=nobs, type='boot', parallel="muticore")
## End(Not run)
``` |

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