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 (styled to the active console theme; plain when colours are
disabled). 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 0.894 0.798 0.597
#> F2 0.888 0.788 0.576
#> F3 0.883 0.780 0.561
summary(fs)
#>
#> ── Factor scores (regression) ──────────────────────────────────────────────────
#>
#> Weights and diagnostics only (correlation-matrix input; no scores).
#>
#> ── Score determinacy ───────────────────────────────────────────────────────────
#>
#> rho rho2 guttman
#> F1 0.894 0.798 0.597
#> F2 0.888 0.788 0.576
#> F3 0.883 0.780 0.561
#>
#> ── Factor weights ──────────────────────────────────────────────────────────────
#>
#> F1 F2 F3
#> V1 0.016 0.037 0.206
#> V2 0.023 0.036 0.146
#> V3 0.038 0.033 0.140
#> V4 0.060 0.025 0.194
#> V5 0.062 0.014 0.138
#> V6 0.009 0.013 0.247
#> V7 0.024 0.177 0.053
#> V8 0.017 0.187 0.031
#> V9 0.031 0.173 0.020
#> V10 0.016 0.222 -0.002
#> V11 0.026 0.115 0.084
#> V12 0.035 0.240 0.030
#> V13 0.202 0.051 0.010
#> V14 0.163 0.002 0.050
#> V15 0.177 0.059 0.007
#> V16 0.170 0.006 0.050
#> V17 0.214 0.013 0.016
#> V18 0.173 0.025 0.041
#>
#> ── Score validity and univocality ──────────────────────────────────────────────
#>
#> Diagonal: validity (score-factor correlation). Off-diagonal: univocality.
#>
#> F1 F2 F3
#> F1 0.894 0.638 0.668
#> F2 0.643 0.888 0.650
#> F3 0.676 0.653 0.883
#>
#> ── Score intercorrelations ─────────────────────────────────────────────────────
#>
#> F1 F2 F3
#> F1 1.000 0.719 0.756
#> F2 0.719 1.000 0.735
#> F3 0.756 0.735 1.000
# format() returns the same lines as plain text:
writeLines(format(fs))
#>
#> ── Factor scores (regression) ──────────────────────────────────────────────────
#>
#> Weights and diagnostics only (correlation-matrix input; no scores).
#>
#> ── Score determinacy ───────────────────────────────────────────────────────────
#>
#> rho rho2 guttman
#> F1 0.894 0.798 0.597
#> F2 0.888 0.788 0.576
#> F3 0.883 0.780 0.561