KMO: Kaiser-Meyer-Olkin criterion

In EFAtools: Fast and Flexible Implementations of Exploratory Factor Analysis Tools

Kaiser-Meyer-Olkin criterion

Description

KMO() has been superseded by efa_kmo(), which is the recommended interface going forward. It remains available and unchanged so existing code keeps working.

Usage

KMO(
  x,
  use = c("pairwise.complete.obs", "all.obs", "complete.obs", "everything",
    "na.or.complete"),
  cor_method = c("pearson", "spearman", "kendall", "poly", "tetra")
)

Arguments

x	data.frame or matrix. Dataframe or matrix of raw data or matrix with correlations.
use	character. The missing-data policy for raw data. Passed to stats::cor() for "pearson", "spearman", and "kendall"; for "poly" / "tetra" the same policies are applied to the raw data before the polychoric estimation, where "all.obs" and "everything" abort on a missing value instead of returning NA correlations. Default is "pairwise.complete.obs".
cor_method	character. Correlation computed from raw data: "pearson", "spearman", or "kendall" (passed to stats::cor()), or "poly" / "tetra" for polychoric / tetrachoric correlations of ordinal / binary data (a two-step estimator). Default is "pearson".

Value

A list of class c("efa_kmo", "KMO"), identical to the value of efa_kmo(); see there for the components.

See Also

efa_kmo()

EFAtools documentation built on Aug. 21, 2026, 5:16 p.m.