kmo_optimal_solution: Calculates the Optimal Solution for Kayser-Meyer-Olkin (KMO)...

In FactorAssumptions: Set of Assumptions for Factor and Principal Component Analysis

Calculates the Optimal Solution for Kayser-Meyer-Olkin (KMO) in a Dataframe

Description

kmo_optimal_solution() call upon the kmo function to iterate over the variables of a dataframe.

Usage

kmo_optimal_solution(df, squared = TRUE)

Arguments

df	a dataframe with only int or num type of variables
squared	TRUE if matrix is squared (such as adjacency matrices), FALSE otherwise

Details

If finds any individual KMO's below the optimal value of 0.5 then removes the lowest KMO value variable until no more variable has not-optimal KMO values.

Value

A list with

1. df - A dataframe that has reached its optimal solution in terms of KMO values

2. removed - A list of removed variables ordened by the first to last removed during the procedure

3. kmo_results - Results of the final iteration of the kmo function

See Also

kmo for kmo computation function

Examples

set.seed(123)
df <- as.data.frame(matrix(rnorm(100*10, 1, .5), ncol=10))
kmo_optimal_solution(df, squared = FALSE)

FactorAssumptions documentation built on March 18, 2022, 5:52 p.m.