| plotRMSEApowernested {semTools} | R Documentation |
Plot power of nested model RMSEA over a range of possible sample sizes.
plotRMSEApowernested(rmsea0A = NULL, rmsea0B = NULL, rmsea1A, rmsea1B = NULL, dfA, dfB, nlow, nhigh, steps=1, alpha=.05, group=1, ...)
rmsea0A |
The H0 baseline RMSEA. |
rmsea0B |
The H0 alternative RMSEA (trivial misfit). |
rmsea1A |
The H1 baseline RMSEA. |
rmsea1B |
The H1 alternative RMSEA (target misfit to be rejected). |
dfA |
degree of freedom of the more-restricted model. |
dfB |
degree of freedom of the less-restricted model. |
nlow |
Lower bound of sample size. |
nhigh |
Upper bound of sample size. |
steps |
Step size. |
alpha |
The alpha level. |
group |
The number of group in calculating RMSEA. |
... |
The additional arguments for the plot function. |
Bell Clinton; Pavel Panko (Texas Tech University; pavel.panko@ttu.edu); Sunthud Pornprasertmanit (psunthud@gmail.com)
MacCallum, R. C., Browne, M. W., & Cai, L. (2006). Testing differences between nested covariance structure models: Power analysis and null hypotheses. Psychological Methods, 11, 19-35.
findRMSEApowernested to find the power for a given sample size in nested model comparison based on population RMSEA
findRMSEAsamplesizenested to find the minium sample size for a given statistical power in nested model comparison based on population RMSEA
plotRMSEApowernested(rmsea0A = 0, rmsea0B = 0, rmsea1A = 0.06, rmsea1B = 0.05, dfA=22, dfB=20, nlow=50, nhigh=500, steps=1, alpha=.05, group=1)