EFAindex {EFAfactors} | R Documentation |
Various Indeces in EFA
Description
A function performs clustering on items by calling VSS and fa. Apply the Very Simple Structure (VSS), Comparative Fit Index (CFI), MAP, and other criteria to determine the appropriate number of factors.
Usage
EFAindex(
response,
nfact.max = 10,
cor.type = "cor",
use = "pairwise.complete.obs"
)
Arguments
response |
A required |
nfact.max |
The maximum number of factors discussed by CD approach. (default = 10) |
cor.type |
How to find the correlations: "cor" is Pearson", "cov" is covariance, "tet" is tetrachoric, "poly" is polychoric, "mixed" uses mixed cor for a mixture of tetrachorics, polychorics, Pearsons, biserials, and polyserials, Yuleb is Yulebonett, Yuleq and YuleY are the obvious Yule coefficients as appropriate. |
use |
an optional character string giving a method for computing covariances in the presence of missing values. This must be one of the strings "everything", "all.obs", "complete.obs", "na.or.complete", or "pairwise.complete.obs" (default). @seealso cor. |
Value
A matrix
with the following components:
- CFI
the Comparative Fit Index
- RMSEA
Root Mean Square Error of Approximation (RMSEA) for each number of factors.
- SRMR
Standardized Root Mean Square Residual.
- MAP
Velicer's MAP values (lower values are better).
- BIC
Bayesian Information Criterion (BIC) for each number of factors.
- SABIC
Sample-size Adjusted Bayesian Information Criterion (SABIC) for each number of factors.
- chisq
Chi-square statistic from the factor analysis output.
- df
Degrees of freedom.
- prob
Probability that the residual matrix is greater than 0.
- eChiSq
Empirically found chi-square statistic.
- eCRMS
Empirically found mean residual corrected for degrees of freedom.
- eBIC
Empirically found BIC based on the empirically found chi-square statistic.
- vss
VSS fit with complexity 1.
- sqresid
Squared residual correlations.
- fit
Factor fit of the complete model.
Examples
library(EFAfactors)
set.seed(123)
##Take the data.bfi dataset as an example.
data(data.bfi)
response <- as.matrix(data.bfi[, 1:25]) ## loading data
response <- na.omit(response) ## Remove samples with NA/missing values
## Transform the scores of reverse-scored items to normal scoring
response[, c(1, 9, 10, 11, 12, 22, 25)] <- 6 - response[, c(1, 9, 10, 11, 12, 22, 25)] + 1
## Run EFAindex function with default parameters.
EFAindex.matrix <- EFAindex(response)
print(EFAindex.matrix)