getCIwidth: Find confidence interval width
In simsem: SIMulated Structural Equation Modeling

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`	`SimResult` that saves the analysis results from multiple replications
`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 `SimResult` with varying sample sizes or percent missing across replications
`pmMCARval`	The percent missing completely at random value that researchers wish to find the confidence interval width from. This argument is applicable for `SimResult` with varying sample sizes or percent missing across replications
`pmMARval`	The percent missing at random value that researchers wish to find the confidence interval width from. This argument is applicable for `SimResult` with varying sample sizes or percent missing across replications
`df`	The degree of freedom used in spline method in predicting the confidence interval width by the predictors. If `df` is 0, the spline method will not be applied. This argument is applicable for `SimResult` with varying sample sizes or percent missing across replications

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, 127-148.

SimResult for a detail of simResult

## 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 fine-grained 
# 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)