kmo_optimal_solution {FactorAssumptions} | R Documentation |
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 |
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
-
df
- A dataframe that has reached its optimal solution in terms of KMO values -
removed
- A list of removed variables ordened by the first to last removed during the procedure -
kmo_results
- Results of the final iteration of thekmo
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)
[Package FactorAssumptions version 2.0.1 Index]