Summarizing EFA Fits
summary.efaList.RdS3 summary and print methods for class efaList.
Usage
lav_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, ...)
lav_efalist_summary_print(x, nd = 3L, cutoff = 0.3, dot.cutoff = 0.1,
alpha.level = 0.01, ...)Arguments
- object
An object of class
efaList, usually, a result of a call toefawith (the default)output = "efa".- x
An object of class
lav_efalist_summary, usually, a result of a call tolav_efalist_summary.- nd
Integer. The number of digits that are printed after the decimal point in the output.
- cutoff
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.
- dot.cutoff
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.
- alpha.level
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.- lambda
Logical. If
TRUE, include the (standardized) factor loadings in the summary.- theta
Logical. If
TRUE, include the unique variances and the communalities in the table of factor loadings.- psi
Logical. If
TRUE, include the factor correlations in the summary. Ignored if only a single factor is used.- fit.table
Logical. If
TRUE, show fit information for each model.- fs.determinacy
Logical. If
TRUE, show the factor score determinacy values per factor (assuming regression factor scores are used) and their squared values.- eigenvalues
Logical. If
TRUE, include the eigenvalues of the sample variance-covariance matrix in the summary.- sumsq.table
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.- lambda.structure
Logical. If
TRUE, show the structure matrix (i.e., the factor loadings multiplied by the factor correlations).- se
Logical. If
TRUE, include the standard errors of the standardized lambda, theta and psi elements in the summary.- zstat
Logical. If
TRUE, include the Z-statistics of the standardized lambda, theta and psi elements in the summary.- pvalue
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.
Value
The function lav_efalist_summary computes and returns a list of
summary statistics for the list of EFA models in object.
Examples
## 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)
#> This is lavaan 0.6-21 -- running exploratory factor analysis
#>
#> Estimator ML
#> Rotation method GEOMIN OBLIQUE
#> Geomin epsilon 0.01
#> Rotation algorithm (rstarts) GPA (1)
#> Standardized metric TRUE
#> Row weights None
#>
#> Number of observations 301
#>
#> Overview models:
#> aic bic sabic chisq df pvalue cfi rmsea
#> nfactors = 1 7738.448 7805.176 7748.091 312.264 27 0.000 0.677 0.187
#> nfactors = 2 7572.491 7668.876 7586.418 130.306 19 0.000 0.874 0.140
#> nfactors = 3 7479.081 7601.416 7496.758 22.897 12 0.029 0.988 0.055
#>
#> Eigenvalues correlation matrix:
#>
#> ev1 ev2 ev3 ev4 ev5 ev6 ev7 ev8 ev9
#> 3.216 1.639 1.365 0.699 0.584 0.500 0.473 0.286 0.238
#>
#> Number of factors: 1
#>
#> Standardized loadings: (* = significant at 1% level)
#>
#> f1 unique.var communalities
#> x1 0.438* 0.808 0.192
#> x2 0.220* 0.951 0.049
#> x3 0.223* 0.950 0.050
#> x4 0.848* 0.281 0.719
#> x5 0.841* 0.293 0.707
#> x6 0.838* 0.298 0.702
#> x7 .* 0.967 0.033
#> x8 0.201* 0.960 0.040
#> x9 0.307* 0.906 0.094
#>
#> f1
#> Sum of squared loadings 2.586
#> Proportion of total 1.000
#> Proportion var 0.287
#> Cumulative var 0.287
#>
#> Standardized structure (= LAMBDA %*% PSI):
#>
#> f1
#> x1 0.438
#> x2 0.220
#> x3 0.223
#> x4 0.848
#> x5 0.841
#> x6 0.838
#> x7 0.180
#> x8 0.201
#> x9 0.307
#>
#> P-values standardized loadings:
#>
#> f1
#> x1 0.000
#> x2 0.000
#> x3 0.000
#> x4 0.000
#> x5 0.000
#> x6 0.000
#> x7 0.002
#> x8 0.001
#> x9 0.000
#>
#> P-values unique variances:
#>
#> x1 x2 x3 x4 x5 x6 x7 x8 x9
#> 0 0 0 0 0 0 0 0 0
#>
#> P-values factor correlations:
#>
#> f1
#> f1 .
#>
#> Number of factors: 2
#>
#> Standardized loadings: (* = significant at 1% level)
#>
#> f1 f2 unique.var communalities
#> x1 0.261* 0.430* 0.673 0.327
#> x2 . 0.251* 0.906 0.094
#> x3 0.455* 0.783 0.217
#> x4 0.850* 0.274 0.726
#> x5 0.867* 0.264 0.736
#> x6 0.824* 0.302 0.698
#> x7 0.447* 0.802 0.198
#> x8 . 0.626* 0.630 0.370
#> x9 0.732* 0.458 0.542
#>
#> f1 f2 total
#> Sum of sq (obliq) loadings 2.281 1.628 3.909
#> Proportion of total 0.584 0.416 1.000
#> Proportion var 0.253 0.181 0.434
#> Cumulative var 0.253 0.434 0.434
#>
#> Factor correlations: (* = significant at 1% level)
#>
#> f1 f2
#> f1 1.000
#> f2 0.331* 1.000
#>
#> Standardized structure (= LAMBDA %*% PSI):
#>
#> f1 f2
#> x1 0.403 0.516
#> x2 0.196 0.288
#> x3 0.180 0.465
#> x4 0.852 0.287
#> x5 0.857 0.258
#> x6 0.835 0.306
#> x7 0.140 0.445
#> x8 0.148 0.606
#> x9 0.254 0.736
#>
#> P-values standardized loadings:
#>
#> f1 f2
#> x1 0.000 0.000
#> x2 0.079 0.000
#> x3 0.610 0.000
#> x4 0.000 0.839
#> x5 0.000 0.367
#> x6 0.000 0.331
#> x7 0.890 0.000
#> x8 0.228 0.000
#> x9 0.717 0.000
#>
#> P-values unique variances:
#>
#> x1 x2 x3 x4 x5 x6 x7 x8 x9
#> 0 0 0 0 0 0 0 0 0
#>
#> P-values factor correlations:
#>
#> f1 f2
#> f1 .
#> f2 0 .
#>
#> Number of factors: 3
#>
#> Standardized loadings: (* = significant at 1% level)
#>
#> f1 f2 f3 unique.var communalities
#> x1 0.604* .* 0.513 0.487
#> x2 0.507* . 0.749 0.251
#> x3 0.691* . 0.543 0.457
#> x4 0.839* 0.279 0.721
#> x5 . 0.887* 0.243 0.757
#> x6 . 0.806* 0.305 0.695
#> x7 . 0.726* 0.502 0.498
#> x8 . 0.703* 0.469 0.531
#> x9 0.368* 0.463* 0.543 0.457
#>
#> f2 f1 f3 total
#> Sum of sq (obliq) loadings 2.226 1.345 1.284 4.855
#> Proportion of total 0.458 0.277 0.264 1.000
#> Proportion var 0.247 0.149 0.143 0.539
#> Cumulative var 0.247 0.397 0.539 0.539
#>
#> Factor correlations: (* = significant at 1% level)
#>
#> f1 f2 f3
#> f1 1.000
#> f2 0.327* 1.000
#> f3 0.278 0.230* 1.000
#>
#> Standardized structure (= LAMBDA %*% PSI):
#>
#> f1 f2 f3
#> x1 0.674 0.392 0.240
#> x2 0.488 0.182 0.031
#> x3 0.673 0.158 0.195
#> x4 0.301 0.849 0.207
#> x5 0.228 0.868 0.196
#> x6 0.341 0.830 0.198
#> x7 0.062 0.149 0.692
#> x8 0.286 0.151 0.722
#> x9 0.505 0.252 0.571
#>
#> P-values standardized loadings:
#>
#> f1 f2 f3
#> x1 0.000 0.005 0.447
#> x2 0.000 0.388 0.089
#> x3 0.000 0.184 0.662
#> x4 0.355 0.000 0.826
#> x5 0.078 0.000 0.740
#> x6 0.067 0.000 0.779
#> x7 0.195 0.356 0.000
#> x8 0.391 0.338 0.000
#> x9 0.002 0.388 0.000
#>
#> P-values unique variances:
#>
#> x1 x2 x3 x4 x5 x6 x7 x8 x9
#> 0 0 0 0 0 0 0 0 0
#>
#> P-values factor correlations:
#>
#> f1 f2 f3
#> f1 .
#> f2 0.000 .
#> f3 0.032 0.010 .
#>