Description Usage Arguments Value Author(s) References See Also Examples
Find the median of confidence interval width or a confidence interval value given a degree of assurance (Lai & Kelley, 2011)
1 2  getCIwidth(object, assurance = 0.50, nVal = NULL, pmMCARval = NULL,
pmMARval = NULL, df = 0)

object 

assurance 
The percentile of the resulting confidence interval width. When assurance is 0.50, the median of the widths is provided. See Lai & Kelley (2011) for more details. 
nVal 
The sample size value that researchers wish to find the confidence interval width from. This argument is applicable for 
pmMCARval 
The percent missing completely at random value that researchers wish to find the confidence interval width from. This argument is applicable for 
pmMARval 
The percent missing at random value that researchers wish to find the confidence interval width from. This argument is applicable for 
df 
The degree of freedom used in spline method in predicting the confidence interval width by the predictors. If 
The median of confidence interval width or a confidence interval given a degree of assurance
Sunthud Pornprasertmanit (psunthud@gmail.com)
Lai, K., & Kelley, K. (2011). Accuracy in parameter estimation for targeted effects in structural equation modeling: Sample size planning for narrow confidence intervals. Psychological Methods, 16, 127148.
SimResult
for a detail of simResult
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31  ## Not run:
loading < matrix(0, 6, 2)
loading[1:3, 1] < NA
loading[4:6, 2] < NA
loadingValues < matrix(0, 6, 2)
loadingValues[1:3, 1] < 0.7
loadingValues[4:6, 2] < 0.7
LY < bind(loading, loadingValues)
latent.cor < matrix(NA, 2, 2)
diag(latent.cor) < 1
RPS < binds(latent.cor, 0.5)
error.cor < matrix(0, 6, 6)
diag(error.cor) < 1
RTE < binds(error.cor)
CFA.Model < model(LY = LY, RPS = RPS, RTE = RTE, modelType="CFA")
# We make the examples running only 5 replications to save time.
# In reality, more replications are needed.
Output < sim(5, n = 200, model=CFA.Model)
# Get the cutoff (critical value) when alpha is 0.05
getCIwidth(Output, assurance=0.80)
# Finding the cutoff when the sample size is varied. Note that more finegrained
# values of n is needed, e.g., n=seq(50, 500, 1)
Output2 < sim(NULL, model=CFA.Model, n=seq(50, 100, 10))
# Get the fit index cutoff when sample size is 75.
getCIwidth(Output2, assurance=0.80, nVal = 75)
## End(Not run)

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