S3 summary and print methods for class efaList.
# S3 method for efaList
summary(object,
nd = 3L, cutoff = 0.3, dot.cutoff = 0.1, alpha.level = 0.01,
lambda = TRUE, theta = TRUE, psi = TRUE, fit.table = TRUE,
fs.determinacy = FALSE, eigenvalues = TRUE, sumsq.table = TRUE,
lambda.structure = FALSE, se = FALSE, zstat = FALSE,
pvalue = FALSE, ...)# S3 method for efaList.summary
print(x, nd = 3L, cutoff = 0.3, dot.cutoff = 0.1,
alpha.level = 0.01, ...)
The function summary.efaList computes and returns a list of
summary statistics for the list of EFA models in object.
An object of class efaList, usually, a result of a call
to efa with (the default) output = "efa".
An object of class summary.efaList, usually, a result of a
call to summary.efaList.
Integer. The number of digits that are printed after the decimal point in the output.
Numeric. Factor loadings smaller that this value (in absolute value) are not printed (even if they are significantly different from zero). The idea is that only medium to large factor loadings are printed, to better see the overall structure.
Numeric. Factor loadings larger (in absolute value) than this value, but smaller (in absolute value) than the cutoff value are shown as a dot. They represent small loadings that may still need your attention.
Numeric. If the the p-value of a factor loading is smaller
than this value, a significance star is printed to the right of the
factor loading. To switch this off, use alpha.level = 0.
Logical. If TRUE, include the (standardized) factor
loadings in the summary.
Logical. If TRUE, include the unique variances and
the communalities in the table of factor loadings.
Logical. If TRUE, include the factor correlations in the
summary. Ignored if only a single factor is used.
Logical. If TRUE, show fit information for each
model.
Logical. If TRUE, show the factor score
determinacy values per factor (assuming regression factor scores are used)
and their squared values.
Logical. If TRUE, include the eigenvalues of the
sample variance-covariance matrix in the summary.
Logical. If TRUE, include a table including sums
of squares of factor loadings (and related measures) in the summary.
The sums of squares are computed as the
diagonal elements of Lambda times Psi (where Psi is the matrix of
factor correlations.).
If orthogonal rotation was used, Psi is diagonal and the sums of
squares are identical to the sums of the squared column elements of the
Lambda matrix (i.e., the factor loadings). This is no longer the case
when obique rotation has been used. But in both cases (orthgonal or
oblique), the (total) sum of the sums of squares equals the sum of the
communalities. In the second row of the table (Proportion of total),
the sums of squares are divided by the total. In the third row of the
table (Proportion var), the sums of squares are divided by the
number of items.
Logical. If TRUE, show the structure matrix
(i.e., the factor loadings multiplied by the factor correlations).
Logical. If TRUE, include the standard errors of the
standardized lambda, theta and psi elements in the summary.
Logical. If TRUE, include the Z-statistics of the
standardized lambda, theta and psi elements in the summary.
Logical. If TRUE, include the P-values of the
standardized lambda, theta and psi elements in the summary.
Further arguments passed to or from other methods.
## The famous Holzinger and Swineford (1939) example
fit <- efa(data = HolzingerSwineford1939,
ov.names = paste("x", 1:9, sep = ""),
nfactors = 1:3,
rotation = "geomin",
rotation.args = list(geomin.epsilon = 0.01, rstarts = 1))
summary(fit, nd = 3L, cutoff = 0.2, dot.cutoff = 0.05,
lambda.structure = TRUE, pvalue = TRUE)
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