PA.RMSEA | R Documentation |
Performs sample size planning by power analysis on RMSEA.
PA.RMSEA(df, method = c("exact.fit", "close.fit", "not.close.fit"), H0rmsea, HArmsea, power = 0.8, alpha = 0.05)
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. |
Return the necessary sample size that achieves the desired power.
Tzu-Yao Lin
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
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
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