print() shows a concise overview of an efa_scores() result: a header naming
the method and whether factor scores were computed, and the per-factor
determinacy table (determinacy, squared determinacy, and Guttman index).
summary() returns a summary.efa_scores object whose print method adds the
full factor-weight matrix, the score validity/univocality matrix, and the score
intercorrelations. format() assembles the same report and returns it as a
character vector; print() is cat(format(x), sep = "\n"). The lines follow
the active console theme, so they are plain when colours are disabled (for
example when captured into a file or stripped with cli::ansi_strip()).
Usage
# S3 method for class 'efa_scores'
print(x, digits = 3, ...)
# S3 method for class 'efa_scores'
format(x, digits = 3, ...)
# S3 method for class 'efa_scores'
summary(object, digits = 3, ...)
# S3 method for class 'summary.efa_scores'
print(x, ...)
# S3 method for class 'summary.efa_scores'
format(x, digits = x$opts$digits, ...)Arguments
- x, object
An object of class
efa_scores; for thesummary.efa_scoresmethods, the object returned bysummary().- digits
numeric. Number of decimal places for the printed tables. Default is 3.
- ...
Not used; for consistency with the generics.
Value
print() and the print method for summary.efa_scores objects return
their argument invisibly. format() returns a character vector with the
report lines. summary() returns an object of class summary.efa_scores.
See also
Other factor scoring:
efa_scores()
Examples
efa <- efa_fit(test_models$baseline$cormat, n_factors = 3, N = 500,
estimator = "PAF", rotation = "oblimin")
fs <- efa_scores(test_models$baseline$cormat, f = efa)
#> ℹ `x` is a correlation matrix; factor scores cannot be computed. Only factor
#> weights and score diagnostics are returned. Enter raw data to get factor
#> scores.
fs
#>
#> ── Factor scores (regression) ──────────────────────────────────────────────────
#>
#> Weights and diagnostics only (correlation-matrix input; no scores).
#>
#> ── Score determinacy ───────────────────────────────────────────────────────────
#>
#> rho rho2 guttman
#> F1 .894 .798 .597
#> F2 .888 .788 .576
#> F3 .883 .780 .561
summary(fs)
#>
#> ── Factor scores (regression) ──────────────────────────────────────────────────
#>
#> Weights and diagnostics only (correlation-matrix input; no scores).
#>
#> ── Score determinacy ───────────────────────────────────────────────────────────
#>
#> rho rho2 guttman
#> F1 .894 .798 .597
#> F2 .888 .788 .576
#> F3 .883 .780 .561
#>
#> ── Factor weights ──────────────────────────────────────────────────────────────
#>
#> F1 F2 F3
#> V1 .016 .037 .206
#> V2 .023 .036 .146
#> V3 .038 .033 .140
#> V4 .060 .025 .194
#> V5 .062 .014 .138
#> V6 .009 .013 .247
#> V7 .024 .177 .053
#> V8 .017 .187 .031
#> V9 .031 .173 .020
#> V10 .016 .222 -.002
#> V11 .026 .115 .084
#> V12 .035 .240 .030
#> V13 .202 .051 .010
#> V14 .163 .002 .050
#> V15 .177 .059 .007
#> V16 .170 .006 .050
#> V17 .214 .013 .016
#> V18 .173 .025 .041
#>
#> ── Score validity and univocality ──────────────────────────────────────────────
#>
#> Diagonal: validity (score-factor correlation). Off-diagonal: univocality.
#>
#> F1 F2 F3
#> F1 .894 .638 .668
#> F2 .643 .888 .650
#> F3 .676 .653 .883
#>
#> ── Score intercorrelations ─────────────────────────────────────────────────────
#>
#> F1 F2 F3
#> F1 1.000 .719 .756
#> F2 .719 1.000 .735
#> F3 .756 .735 1.000
# format() returns the same lines as a character vector:
writeLines(format(fs))
#>
#> ── Factor scores (regression) ──────────────────────────────────────────────────
#>
#> Weights and diagnostics only (correlation-matrix input; no scores).
#>
#> ── Score determinacy ───────────────────────────────────────────────────────────
#>
#> rho rho2 guttman
#> F1 .894 .798 .597
#> F2 .888 .788 .576
#> F3 .883 .780 .561