extractor.feature.FF: Extracting features According to Goretzko & Buhner (2020)

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

Extracting features According to Goretzko & Buhner (2020)

Description

This function will extract 181 features from the data according to the method by Goretzko & Buhner (2020).

Usage

extractor.feature.FF(
  response,
  cor.type = "pearson",
  use = "pairwise.complete.obs"
)

Arguments

response	A required N × I matrix or data.frame consisting of the responses of N individuals to I items.
cor.type	A character string indicating which correlation coefficient (or covariance) is to be computed. One of "pearson" (default), "kendall", or "spearman". @seealso cor.
use	An optional character string giving a method for computing covariances in the presence of missing values. This must be one of the strings "everything", "all.obs", "complete.obs", "na.or.complete", or "pairwise.complete.obs" (default). @seealso cor.

Details

The code for the extractor.feature.FF function is implemented based on the publicly available code by Goretzko & Buhner (2020) (https://osf.io/mvrau/). The extracted features are completely consistent with the 181 features described in the original text by Goretzko & Buhner (2020). These features include:

• 1. - Number of examinees

• 2. - Number of items

• 3. - Number of eigenvalues greater than 1

• 4. - Proportion of variance explained by the 1st eigenvalue

• 5. - Proportion of variance explained by the 2nd eigenvalue

• 6. - Proportion of variance explained by the 3rd eigenvalue

• 7. - Number of eigenvalues greater than 0.7

• 8. - Standard deviation of the eigenvalues

• 9. - Number of eigenvalues accounting for 50

• 10. - Number of eigenvalues accounting for 75

• 11. - L1-norm of the correlation matrix

• 12. - Frobenius-norm of the correlation matrix

• 13. - Maximum-norm of the correlation matrix

• 14. - Average of the off-diagonal correlations

• 15. - Spectral-norm of the correlation matrix

• 16. - Number of correlations smaller or equal to 0.1

• 17. - Average of the initial communality estimates

• 18. - Determinant of the correlation matrix

• 19. - Measure of sampling adequacy (MSA after Kaiser, 1970)

• 20. - Gini coefficient (Gini, 1921) of the correlation matrix

• 21. - Kolm measure of inequality (Kolm, 1999) of the correlation matrix

• 22-101. - Eigenvalues from Principal Component Analysis (PCA), padded with -1000 if insufficient

• 102-181. - Eigenvalues from Factor Analysis (FA), fixed at 1 factor, padded with -1000 if insufficient

Value

A matrix (1×181) containing all the 181 features (Goretzko & Buhner, 2020).

References

Goretzko, D., & Buhner, M. (2020). One model to rule them all? Using machine learning algorithms to determine the number of factors in exploratory factor analysis. Psychol Methods, 25(6), 776-786. https://doi.org/10.1037/met0000262.

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


## Run extractor.feature.FF function with default parameters.

 features <- extractor.feature.FF(response)

 print(features)

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