Swamy, A. Burmawala
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This function computes the Kaiser-Meyer-Olkin (KMO) criterion overall and for each variable in a correlation matrix. The KMO represents the degree to which each observed variable is predicted by the other variables in the dataset and with this indicates the suitability for factor analysis.
Kaiser (1970) proposed this index, originally called measure of sampling adequacy (MSA), that indicates how near the inverted correlation matrix R^{-1} is to a diagonal matrix S to determine a given correlation matrix's (R) suitability for factor analysis. The index is
KMO = \frac{\sum\limits_{i with Q = SR^{-1}S and S = (diag R^{-1})^{-1/2} where
\sum\limits_{i So KMO varies between 0 and 1, with larger values indicating higher suitability
for factor analysis. Kaiser and Rice (1974) suggest that KMO should at least
exceed .50 for a correlation matrix to be suitable for factor analysis. This function was heavily influenced by the psych::KMO
function. See also BARTLETT for another test of suitability for factor
analysis. The KMO function can also be called together with the
BARTLETT function and with factor retention criteria in the
N_FACTORS function. A list containing Kaiser, H. F. (1970). A second generation little jiffy. Psychometrika,
35, 401-415. Kaiser, H. F. & Rice, J. (1974). Little jiffy, mark IV. Educational
and Psychological Measurement, 34, 111-117. Cureton, E. E. & D'Augustino, R. B. (1983). Factor analysis: An
applied approach. Hillsdale, N.J.: Lawrence Erlbaum Associates, Inc. BARTLETT for another measure to determine
suitability for factor analysis. N_FACTORS as a wrapper function for this function,
BARTLETT and several factor retention criteria.
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What is kmo in factor analysis?
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