mifa_ci_fieller: Fieller confidence intervals for explained variance
In mifa: Multiple Imputation for Exploratory Factor Analysis

Description Usage Arguments Details Value References See Also Examples

View source: R/mifa-ci.R

Computes parametric confidence intervals for proportion of explained variance for given numbers of principal components using Fieller's method. Note that by setting ci = TRUE in mifa(), this confidence interval can be computed as well.

1	mifa_ci_fieller(cov_imps, n_pc, conf = 0.95, N)

`cov_imps`	List containing the estimated covariance matrix within each imputed data. One can use `cov_imputations` returned by `mifa()`.
`n_pc`	Integer or integer vector indicating number of principal components (eigenvectors) for which explained variance (eigenvalues) should be obtained and for which confidence intervals should be computed. Defaults to all principal components, i.e., the number of variables in the data.
`conf`	Confidence level for constructing confidence intervals. The default is `.95` that is, 95% confidence intervals.
`N`	A scalar specifying sample size.

Normally, this function does not need to be called directly. Instead, use mifa(..., ci = "fieller").

A data frame containing confidence intervals for n_pc principal components.

Fieller, E. C. (1954). Some problems in interval estimation. Journal of the Royal Statistical Society. Series B (Methodological): 175-185.

mifa()

Other mifa confidence intervals: mifa_ci_boot()

if(requireNamespace("psych")) {
  data <- psych::bfi[, 1:25]
  mi <- mifa(data, print = FALSE)
  mifa_ci_fieller(mi$cov_imputations, n_pc = 3:8, N = nrow(data))
}