getCIwidth function

Find confidence interval width

Find confidence interval width

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

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

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

Returns

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

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.

Author(s)

Sunthud Pornprasertmanit (psunthud@gmail.com )

See Also

SimResult for a detail of simResult

Examples

## 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)
  • Maintainer: Terrence D. Jorgensen
  • License: GPL (>= 2)
  • Last published: 2021-03-28