Plot EFA K-means Clustering Results

This function creates a plot to visualize the Within-cluster Sum of Squares (WSS) for different numbers of clusters (K) in the context of exploratory factor analysis. The plot helps identify the most appropriate number of factors by showing how WSS decreases as the number of factors (or clusters) increases.

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

Arguments

x

An object of class EFAkmeans, 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

EFAkmeans

Examples

library(EFAfactors)
set.seed(123)

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

response <- as.matrix(data.bfi[, 1:25]) ## Load 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

# \donttest{
  EFAkmeans.obj <- EFAkmeans(response)

  ## Plot the EFA K-means clustering results
  plot(EFAkmeans.obj)

# }