R: Plot the sampling distributions of RMSEA

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 `rmsea`
`rmseaScale` | If `TRUE`, the RMSEA scale is used in the x-axis. If `FALSE`, the chi-square scale is used in the x-axis.
`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-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.

### 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, 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.

### 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(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)

[Package semTools version 0.4-14 Index]