extractor.feature.FF {EFAfactors} | R Documentation |
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 |
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.