View source: R/powerAnalysisRMSEA.R
plotRMSEAdist | 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 χ^2 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 group, and \varepsilon is the population RMSEA. Next, the noncentral χ^2 distribution with a specified df and noncentrality parameter is plotted. Thus, the x-axis represents the sample χ^2 value. The sample χ^2 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 χ^2 value obtained from the noncentral χ^2 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(3), 305–319. doi: 10.1207/s15328007sem1103_1
MacCallum, R. C., Browne, M. W., & Sugawara, H. M. (1996). Power analysis and determination of sample size for covariance structure modeling. Psychological Methods, 1(2), 130–149. doi: 10.1037/1082-989X.1.2.130
Steiger, J. H. (1998). A note on multiple sample extensions of the RMSEA fit index. Structural Equation Modeling, 5(4), 411–419. doi: 10.1080/10705519809540115
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(c(.05, .08), n = 200, df = 20, ptile = .95, rmseaScale = TRUE) plotRMSEAdist(c(.05, .01), n = 200, df = 20, ptile = .05, rmseaScale = FALSE)
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