plotRMSEAdist {semTools} | R Documentation |
Plot the sampling distributions of RMSEA
Description
Plots the sampling distributions of RMSEA based on the noncentral chi-square distributions
Usage
plotRMSEAdist(rmsea, n, df, ptile = NULL, caption = NULL,
rmseaScale = TRUE, group = 1)
Arguments
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. |
Details
This function creates overlappling plots of the sampling distribution of
RMSEA based on noncentral \chi^2
distribution (MacCallum, Browne, &
Suguwara, 1996). First, the noncentrality parameter (\lambda
) is
calculated from RMSEA (Steiger, 1998; Dudgeon, 2004) by
\lambda = (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 \chi^2
distribution with a
specified df and noncentrality parameter is plotted. Thus,
the x-axis represents the sample \chi^2
value. The sample \chi^2
value can be transformed to the sample RMSEA scale (\hat{\varepsilon}
)
by
\hat{\varepsilon} = \sqrt{K}\sqrt{\frac{\chi^2 - d}{(N - 1)d}},
where \chi^2
is the \chi^2
value obtained from the noncentral
\chi^2
distribution.
Author(s)
Sunthud Pornprasertmanit (psunthud@gmail.com)
References
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
See Also
-
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
Examples
plotRMSEAdist(c(.05, .08), n = 200, df = 20, ptile = .95, rmseaScale = TRUE)
plotRMSEAdist(c(.05, .01), n = 200, df = 20, ptile = .05, rmseaScale = FALSE)