| Type: | Package |
| Title: | Graphical Extension with Accuracy in Parameter Estimation (GAIPE) |
| Version: | 1.1 |
| Date: | 2022-05-24 |
| Author: | Tzu-Yao Lin |
| Maintainer: | Yao Lin <zaiyaolin@gmail.com> |
| Depends: | R (≥ 3.4.1) |
| Description: | Implements graphical extension with accuracy in parameter estimation (AIPE) on RMSEA for sample size planning in structural equation modeling based on Lin, T.-Z. & Weng, L.-J. (2014) <doi:10.1080/10705511.2014.915380>. And, it can also implement AIPE on RMSEA and power analysis on RMSEA. |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| URL: | https://www.r-project.org |
| NeedsCompilation: | no |
| Packaged: | 2022-05-25 14:03:30 UTC; yaolin |
| Repository: | CRAN |
| Date/Publication: | 2022-05-25 14:20:05 UTC |
Graphical Extension with Accuracy in Parameter Estimation (GAIPE)
Description
Implements graphical extension with accuracy in parameter estimation (AIPE) on RMSEA for sample size planning in structural equation modeling based on Lin, T.-Z. & Weng, L.-J. (2014) <doi: 10.1080/10705511.2014.915380>.
Details
| Package: | GAIPE |
| Type: | Package |
| Version: | 1.1 |
| Date: | 2022-05-24 |
| License: | GPL (>= 2) |
Author(s)
Tzu-Yao Lin Maintainer: Yao Lin <zaiyaolin@gmail.com>
References
Lin, T.-Z. & Weng, L.-J. (2014) Graphical Extension of Sample Size Planning With AIPE on RMSEA Using R. Structural Equation Modeling, 21, 482-490. doi: 10.1080/10705511.2014.915380
Sample size planning by AIPE approach on RMSEA
Description
Performs sample size planning by AIPE approach for RMSEA.
Usage
AIPE.RMSEA(rmsea, df, width, clevel = 0.95)
Arguments
rmsea |
expected RMSEA. |
df |
model degrees of freedom. |
width |
desired confidence width. |
clevel |
confidence level (e.g., .90, .95, etc.). |
Value
Return the necessary sample size that satisfies the desired width of a confidence interval.
Author(s)
Tzu-Yao Lin
References
Kelley, K., & Lai, K. (2011). Accuracy in parameter estimation for the root mean square error of approximation: Sample size planning for narrow confidence intervals. Multivariate Behavioral Research, 46, 1-32. doi: 10.1080/00273171.2011.543027
Examples
AIPE.RMSEA(rmsea=.05,df=30,width=.02,clevel=.95)
Computing the confidence interval for RMSEA
Description
Computes the confidence interval for RMSEA.
Usage
CI.RMSEA(rmsea,df,N,clevel=.95)
Arguments
rmsea |
expected or observed RMSEA. |
df |
model degrees of freedom. |
N |
sample size. |
clevel |
confidence level (e.g., .90, .95, etc.). |
Value
Return the upper and lower bound as well as the expected or observed value of the RMSEA.
Author(s)
Tzu-Yao Lin
References
Browne, M. W., & Cudeck, R. (1992). Alternative ways of assessing model fit. Sociological Methods and Research, 21(2), 230-258. doi: 10.1177/0049124192021002005
Examples
CI.RMSEA(rmsea=.05,df=30,N=200,clevel=.95)
Sample size planning by GAIPE framework on RMSEA
Description
Draws the graph for sample size planning by GAIPE framework on RMSEA.
Usage
GAIPE.RMSEA(rmsea, df, width = NULL, clevel = 0.95, N = c(100, 1800, 15),
PA_method = c("exact.fit", "close.fit", "not.close.fit"),
H0rmsea = NULL, alpha = 0.05, power = c(0.8, 0.9, 0.95))
Arguments
rmsea |
vector of the expected RMSEA values. |
df |
model degrees of freedom. |
width |
vector of desired confidence interval widths to be highlighted in the graph. |
clevel |
confidence level (e.g., .90, .95, etc.). |
N |
vector of specifying the range and the increment of sample size for drawing confidence intervals. Note that N[1:2] represents the range whereas N[3] represents the increment. |
PA_method |
a character string specifying the desired hypothesis test for power analysis, can be one of "exact.fit", "close.fit", or "not.close.fit". |
H0rmsea |
RMSEA for null hypothesis. |
alpha |
type I error rate for power analysis. |
power |
vector of specifying the power values for which horizontal lines are to be added in the graph. |
Details
If user wants to implement the power analysis based on RMSEA in GAIPE, the PA_method and H0rmsea have to be specified. In such a case, the first value of rmsea is the RMSEA for the alternative hypothesis.
Author(s)
Tzu-Yao Lin
References
Lin, T.-Z. & Weng, L.-J. (2014) Graphical Extension of Sample Size Planning With AIPE on RMSEA Using R. Structural Equation Modeling, 21, 482-490. doi:10.1080/10705511.2014.915380
Examples
# Drawing the graphs in Lin & Weng (2014) #
# FIGURE 2 #
GAIPE.RMSEA(rmsea=.05,df=30,width=c(.03,.04))
# FIGURE 3 #
GAIPE.RMSEA(rmsea=c(.05,.08),df=30,width=c(.03,.04))
# FIGURE 4 #
GAIPE.RMSEA(rmsea=.025,df=30,width=c(.03,.04),PA_method="not.close.fit",H0rmsea=0.05)
# FIGURE 5 #
GAIPE.RMSEA(rmsea=.05,df=30,width=c(.03,.04),PA_method="exact.fit",H0rmsea=0)
Sample size planning by power analysis on RMSEA
Description
Performs sample size planning by power analysis on RMSEA.
Usage
PA.RMSEA(df, method = c("exact.fit", "close.fit", "not.close.fit"),
H0rmsea, HArmsea, power = 0.8, alpha = 0.05)
Arguments
df |
model degrees of freedom. |
method |
a character string specifying the hypothesis test for power analysis, must be one of "exact.fit", "close.fit", or "not.close.fit"(default). |
H0rmsea |
RMSEA for the null hypothesis. |
HArmsea |
RMSEA for the alternative hypothesis. |
power |
desired power value. |
alpha |
Type I error rate. |
Value
Return the necessary sample size that achieves the desired power.
Author(s)
Tzu-Yao Lin
References
Hancock, G. R., & Freeman, M. J. (2001). Power and sample size for the root mean square error of approximation test of not close fit in structural equation modeling. Educational and Psychological Measurement, 61(5), 741-758. doi: 10.1177/00131640121971491
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
Examples
PA.RMSEA(df=30,method="not.close.fit",H0rmsea=.05,HArmsea=.02,power=.8,alpha=.05)
# Reproducing Table 8 in Hancock and Freeman (2001) #
# DF=c(seq(5,100,5),seq(110,200,10),225,250)
# POWER=c(seq(.5,.99,.05),.99)
# out=matrix(NA,length(DF),length(POWER))
# for(i in 1:length(DF)){
# for(j in 1:length(POWER)){
# out[i,j]=PA.RMSEA(df=DF[i],method="not.close.fit",
# H0rmsea=.05,HArmsea=.02,power=POWER[j],alpha=.05)
# }
# }
# colnames(out)=paste("Pi=",POWER,"",sep="")
# rownames(out)=paste("df=",DF,"",sep="")
# out