simsem (version 0.5-17)

getCIwidth: Find confidence interval width

Description

Find the median of confidence interval width or a confidence interval value given a degree of assurance (Lai & Kelley, 2011)

Usage

getCIwidth(object, assurance = 0.50, nVal = NULL, pmMCARval = NULL, 
	pmMARval = NULL, df = 0)

Value

The median of confidence interval width or a confidence interval given a degree of assurance

Arguments

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

Author

Sunthud Pornprasertmanit ()

References

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.

See Also

SimResult for a detail of simResult

Examples

if (FALSE) {
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)
}