| plotRMSEAdist {semTools} | R Documentation |
Plots the sampling distributions of RMSEA based on the noncentral chi-square distributions
plotRMSEAdist(rmsea, n, df, ptile=NULL, caption=NULL, rmseaScale = TRUE, group=1)
rmsea |
The vector of RMSEA values to be plotted |
n |
Sample size of a dataset |
df |
Model degrees of freedom |
ptile |
The percentile rank of the distribution of the first RMSEA that users wish to plot a vertical line in the resulting graph |
caption |
The name vector of each element of |
rmseaScale |
If |
group |
The number of group that is used to calculate RMSEA. |
This function creates overlappling plots of the sampling distribution of RMSEA based on noncentral chi-square distribution (MacCallum, Browne, & Suguwara, 1996). First, the noncentrality parameter (λ) is calculated from RMSEA (Steiger, 1998; Dudgeon, 2004) by
λ = (N - 1)d\varepsilon^2 / K,
where N is sample size, d is the model degree of freedom, K is the number of groupand \varepsilon is the population RMSEA. Next, the noncentral chi-square distribution with a specified degree of freedom and noncentrality parameter is plotted. Thus, the x-axis represent the sample chi-square value. The sample chi-square value can be transformed to the sample RMSEA scale (\hat{\varepsilon}) by
\hat{\varepsilon} = √{K}√{\frac{χ^2 - d}{(N - 1)d}},
where χ^2 is the chi-square value obtained from the noncentral chi-square distribution.
Sunthud Pornprasertmanit (psunthud@gmail.com)
Dudgeon, P. (2004). A note on extending Steiger's (1998) multiple sample RMSEA adjustment to other noncentrality parameter-based statistic. Structural Equation Modeling, 11, 305-319.
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.
Steiger, J. H. (1998). A note on multiple sample extensions of the RMSEA fit index. Structural Equation Modeling, 5, 411-419.
plotRMSEApower to plot the statistical power based on population RMSEA given the sample size
findRMSEApower to find the statistical power based on population RMSEA given a sample size
findRMSEAsamplesize to find the minium sample size for a given statistical power based on population RMSEA
plotRMSEAdist(rmsea=c(.05, .08), n=200, df=20, ptile=0.95, rmseaScale = TRUE) plotRMSEAdist(rmsea=c(.05, .01), n=200, df=20, ptile=0.05, rmseaScale = FALSE)