Kaiser-Meyer-Olkin-Statistics | R Documentation |
description
KMOS(x, use = c("everything", "all.obs", "complete.obs", "na.or.complete", "pairwise.complete.obs")) ## S3 method for class 'MSA_KMO' print(x, stats = c("both", "MSA", "KMO"), vars = "all", sort = FALSE, show = "all", digits = getOption("digits"), ...)
x |
The data X for |
use |
defines the method to use if missing values are present (for a detailed explanation see |
stats |
determines if |
vars |
can be |
sort |
sorts the MSAs in increasing order. |
show |
shows the specified number of variables (from 1 to the number of potentially sorted variables). |
digits |
the number of decimal places to print. |
... |
further arguments. |
The Measure of Sampling Adequacy (MSA) for individual items and the Kaiser-Meyer-Olkin (KMO) Criterion rely on the Anti-Image-Correlation Matrix A (for details see Kaiser & Rice, 1974) that contains all bivariate partial correlations given all other items in the a_ij = r_ij | X \ {i, j} which is:
A = [diag(R⁻¹)]^(-1 ∕ 2) R⁻¹ [diag(R⁻¹)]^(-1 ∕ 2)
where R is the correlation matrix, based on the data X.
The KMO and MSAs for individual items are (adapted from Equations (3) and (4) in Kaiser & Rice, 1974; note that a is q in the article):
KMO = (∑∑ r²_ij) ∕ (∑∑ r²_ij + a²_ij), i ≠ j
MSA_i = (∑_j r²_ij) ∕ (∑_j r²_ij + a²_ij), j ≠ i
Historically, as suggested in Kaiser (1974) and Kaiser & Rice (1974), a rule of thumb for those values is:
≥ .9 | marvelous |
[.8, .9) | meritorious |
[.7, .8) | middling |
[.6, .7) | mediocre |
[.5, .6) | miserable |
< .5 | unacceptable |
A list of class 'MSA_KMO'
call |
the issued function call |
cormat |
correlation matrix |
pcormat |
normalized negative inverse of the correlation matrix (pairwise correlations given all other variables) |
n |
the number of observations |
k |
the number of variables/items |
MSA |
measure of sampling adequacy |
KMO |
Kaiser-Meyer-Olkin criterion |
Marco J. Maier
Kaiser, H. F. (1970). A Second Generation Little Jiffy. Psychometrika, 35(4), 401–415.
Kaiser, H. F. (1974). An Index of Factorial Simplicity. Psychometrika, 39(1), 31–36.
Kaiser, H. F., & Rice, J. (1974). Little Jiffy, Mark IV. Educational and Psychological Measurement, 34, 111–117.
cor
, bart_spher
set.seed(5L) daten <- data.frame("A"=rnorm(100), "B"=rnorm(100), "C"=rnorm(100), "D"=rnorm(100), "E"=rnorm(100)) cor(daten) KMOS(daten, use = "pairwise.complete.obs")
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