semPower is an R-package that provides several functions to perform a-priori, post-hoc, and compromise power analyses for structural equation models (SEM).
Install semPower via CRAN or as follows:
install.packages("devtools")
library("devtools")
install_github("moshagen/semPower")Read the manual by typing
vignette("semPower")Determine the required sample size to detect misspecifications of a model (involving df = 100 degrees of freedom) corresponding to RMSEA = .05 with a power of 80% on an alpha error of .05:
ap <- semPower.aPriori(effect = .05, effect.measure = 'RMSEA', alpha = .05, power = .80, df = 100)
summary(ap)Determine the achieved power with a sample size of N = 1000 to detect misspecifications of a model (involving df = 100 degrees of freedom) corresponding to RMSEA = .05 on an alpha error of .05:
ph <- semPower.postHoc(effect = .05, effect.measure = 'RMSEA', alpha = .05, N = 1000, df = 100)
summary(ph)Determine the critical chi-square such that the associated alpha and beta errors are equal, assuming sample size of N = 1000, a model involving df = 100 degrees of freedom, and misspecifications corresponding to RMSEA = .05:
cp <- semPower.compromise(effect = .05, effect.measure = 'RMSEA', abratio = 1, N = 1000, df = 100)
summary(cp)Plot power as function of the sample size to detect misspecifications corresponding to RMSEA = .05 (assuming df = 100) on alpha = .05:
semPower.powerPlot.byN(effect = .05, effect.measure = 'RMSEA', alpha = .05, df = 100, power.min = .05, power.max = .99)Plot power as function of the magnitude of effect (measured through the RMSEA assuming df = 100) at N = 500 on alpha = .05:
semPower.powerPlot.byEffect(effect.measure = 'RMSEA', alpha = .05, N = 500, df = 100, effect.min = .001, effect.max = .10)For more details and for a description how to express the magnitude of effect in terms of model parameters, see the manual.
If you use semPower in publications, please cite the package as follows:
Moshagen, M., & Erdfelder, E. (2016). A new strategy for testing structural equation models.Structural Equation Modeling, 23, 54-60. doi: 10.1080/10705511.2014.950896