fs.KMO function

Feature selection for KMO

Drop variables if their MSA_i valus is lower than a threshold, in order to increase the overall KMO (MSA) value.

fs.KMO(data,min_MSA=0.5,cor.mtx=FALSE)

Arguments

• data: A numeric data frame
• min_MSA: A numeric value. Minimal MSA value for variable i
• cor.mtx: Boolean value. The input is either a correlation matrix (cor.mtx=TRUE), or not (cor.mtx=FALSE)

Details

Low Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy does not suggest using principal component or factor analysis. Therefore, this function drop variables with low KMO/MSA values.

Returns

• data: Cleaned data or the cleaned correlation matrix.

References

Abonyi, J., Czvetkó, T., Kosztyán, Z. T., & Héberger, K. (2022). Factor analysis, sparse PCA, and Sum of Ranking Differences-based improvements of the Promethee-GAIA multicriteria decision support technique. Plos one, 17(2), e0264277. doi:10.1371/journal.pone.0264277

Author(s)

Zsolt T. Kosztyan*, Marcell T. Kurbucz, Attila I. Katona

e-mail*: kosztyan.zsolt@gtk.uni-pannon.hu

See Also

summary.

Examples

library(psych)
data(I40_2020)
data<-I40_2020
KMO(fs.KMO(data,min_MSA=0.7,cor.mtx=FALSE))