plot.EKC: Plot Empirical Kaiser Criterion (EKC) Plot

In EFAfactors: Determining the Number of Factors in Exploratory Factor Analysis

Plot Empirical Kaiser Criterion (EKC) Plot

Description

This function generates an Empirical Kaiser Criterion (EKC) plot to visualize the eigenvalues of the actual data. The EKC method helps in determining the number of factors to retain by identifying the point where the eigenvalues exceed the reference eigenvalue. The plot provides a graphical representation to assist in factor selection.

Usage

## S3 method for class 'EKC'
plot(x, ...)

Arguments

x	An object of class EKC, representing the results to be plotted.
...	Additional arguments to be passed to the plotting function.

Value

None. This function is used for side effects (plotting).

See Also

EKC

Examples

library(EFAfactors)
set.seed(123)

##Take the data.bfi dataset as an example.
data(data.bfi)

response <- as.matrix(data.bfi[, 1:25]) ## loading data
response <- na.omit(response) ## Remove samples with NA/missing values

## Transform the scores of reverse-scored items to normal scoring
response[, c(1, 9, 10, 11, 12, 22, 25)] <- 6 - response[, c(1, 9, 10, 11, 12, 22, 25)] + 1


 EKC.obj <- EKC(response)

 ## EKC plot
 plot(EKC.obj)

EFAfactors documentation built on Sept. 30, 2024, 1:06 a.m.