| findRMSEApower {semTools} | R Documentation |
Find the proportion of the samples from the sampling distribution of RMSEA in the alternative hypothesis rejected by the cutoff dervied from the sampling distribution of RMSEA in the null hypothesis. This function can be applied for both test of close fit and test of not-close fit (MacCallum, Browne, & Suguwara, 1996)
findRMSEApower(rmsea0, rmseaA, df, n, alpha=.05, group=1)
rmsea0 |
Null RMSEA |
rmseaA |
Alternative RMSEA |
df |
Model degrees of freedom |
n |
Sample size of a dataset |
alpha |
Alpha level used in power calculations |
group |
The number of group that is used to calculate RMSEA. |
This function find the proportion of sampling distribution derived from the alternative RMSEA that is in the critical region derived from the sampling distribution of the null RMSEA. If rmseaA is greater than rmsea0, the test of close fit is used and the critical region is in the right hand side of the null sampling distribution. On the other hand, if rmseaA is less than rmsea0, the test of not-close fit is used and the critical region is in the left hand side of the null sampling distribution (MacCallum, Browne, & Suguwara, 1996).
Sunthud Pornprasertmanit (psunthud@gmail.com)
MacCallum, R. C., Browne, M. W., & Sugawara, H. M. (1996). Power analysis and determination of sample size for covariance structure modeling. Psychological Methods, 1, 130-149.
plotRMSEApower to plot the statistical power based on population RMSEA given the sample size
plotRMSEAdist to visualize the RMSEA distributions
findRMSEAsamplesize to find the minium sample size for a given statistical power based on population RMSEA
findRMSEApower(rmsea0=.05, rmseaA=.08, df=20, n=200)