Set of Assumptions for Factor and Principal Component Analysis. Tests for Kaiser-Meyer-Olkin (KMO) and Communalities in a dataset. It provides a final sample by removing variables in a iterable manner while keeping account of the variables that were removed in each step.



## install devtools if not already
if (!requireNamespace("devtools", quietly = TRUE)) {
## install FactorAssumptions from Github

What is KMO and Communalities?

Factor Analysis and Principal Components Analysis (PCA) have some precautions and assumptions to be observed (Hair et al. (2018)).

The first one is the KMO (Kaiser-Meyer-Olkin) measure, which measures the proportion of variance among the variables that can be derived from the common variance, also called systematic variance. KMO is computed between 0 and 1. Low values (close to 0) indicate that there are large partial correlations in comparison to the sum of the correlations, that is, there is a predominance of correlations of the variables that are problematic for the factorial/principal component analysis. Hair et al. (2018) suggest that individual KMOs smaller than 0.5 be removed from the factorial/principal component analysis. Consequently, this removal causes the overall KMO of the remaining variables of the factor/principal component analysis to be greater than 0.5.

The second assumption of a valid factor or PCA analysis is the communality of the rotated variables. The commonalities indicate the common variance shared by factors/components with certain variables. Greater communality indicated that a greater amount of variance in the variable was extracted by the factorial/principal component solution. For a better measurement of factorial/principal component analysis, communalities should be 0.5 or greater (Hair et al. (2018)).


I encourage you to check the vignette on how to use the package.


Hair, Joseph F., William C. Black, Barry J. Babin, and Rolph E. Anderson. 2018. Multivariate Data Analysis. 8th ed. Cengage Learning.