print() shows a summarised output of the efa_average() function: the
averaging settings, the error/convergence/Heywood/admissibility rates, the
indicator-to-factor correspondences, the averaged loadings (and, for oblique
solutions, the factor intercorrelations), the variances accounted for, and the
model fit. 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()).
Arguments
- x
An object of class
efa_average(output fromefa_average()).- stat
character. A vector with the statistics to print. Possible inputs are "average", "sd", "range", "min", and "max". Default is "average" and "range".
- plot
logical. Whether a plot of the average and min- max loadings should be created. Default is FALSE. If more than 10 factors are extracted, no plot is created. Only used by
print().- ...
Not used; for consistency with the generic.
Value
print() returns its argument x invisibly. format() returns a
character vector with the report lines (styled to the active console theme;
plain when colours are disabled).
Examples
# \donttest{
EFA_aver <- efa_average(test_models$baseline$cormat, n_factors = 3, N = 500)
#>
🏃 Extracting data...
🚶 Reordering factors...
🏃 Averaging data...
Done!
EFA_aver
#>
#> Averaging performed with averaging method mean (trim = 0) across 72 EFAs,
#> varying the following settings: init_comm, criterion_type, k_promax, p_type,
#> and varimax_type.
#>
#> The error rate is at 0%. Of the solutions that did not result in an error, 100%
#> converged. Of the solutions that converged, 0% contained Heywood cases and 100%
#> were admissible.
#>
#> ══ Indicator-to-Factor Correspondences ═════════════════════════════════════════
#>
#> For each cell, the proportion of solutions including the respective
#> indicator-to-factor correspondence. A salience threshold of 0.3 was used to
#> determine indicator-to-factor correspondences.
#>
#> F1 F2 F3
#> V1 .00 .00 1.00
#> V2 .00 .00 1.00
#> V3 .00 .00 1.00
#> V4 .00 .00 1.00
#> V5 .00 .00 1.00
#> V6 .00 .00 1.00
#> V7 .00 1.00 .00
#> V8 .00 1.00 .00
#> V9 .00 1.00 .00
#> V10 .00 1.00 .00
#> V11 .00 1.00 .00
#> V12 .00 1.00 .00
#> V13 1.00 .00 .00
#> V14 1.00 .00 .00
#> V15 1.00 .00 .00
#> V16 1.00 .00 .00
#> V17 1.00 .00 .00
#> V18 1.00 .00 .00
#>
#> ══ Loadings ════════════════════════════════════════════════════════════════════
#>
#> ── Mean ────────────────────────────────────────────────────────────────────────
#>
#> F1 F2 F3
#> V1 -.019 .056 .588
#> V2 .022 .083 .466
#> V3 .079 .073 .440
#> V4 .119 .017 .532
#> V5 .169 .006 .426
#> V6 -.042 -.023 .672
#> V7 .027 .515 .110
#> V8 .010 .558 .051
#> V9 .060 .530 .022
#> V10 .004 .644 -.043
#> V11 .039 .351 .237
#> V12 .046 .625 .016
#> V13 .590 .105 -.035
#> V14 .527 -.039 .104
#> V15 .540 .143 -.039
#> V16 .534 -.022 .108
#> V17 .634 -.010 .000
#> V18 .535 .029 .071
#>
#> ── Range ───────────────────────────────────────────────────────────────────────
#>
#> F1 F2 F3
#> V1 .098 .077 .092
#> V2 .076 .061 .063
#> V3 .064 .064 .052
#> V4 .069 .087 .069
#> V5 .046 .079 .047
#> V6 .108 .097 .121
#> V7 .059 .043 .052
#> V8 .059 .057 .064
#> V9 .048 .052 .072
#> V10 .059 .079 .090
#> V11 .061 .011 .015
#> V12 .058 .067 .084
#> V13 .072 .044 .099
#> V14 .056 .071 .049
#> V15 .062 .035 .098
#> V16 .055 .071 .052
#> V17 .081 .064 .084
#> V18 .055 .060 .064
#>
#> ══ Factor Intercorrelations from Oblique Solutions ═════════════════════════════
#>
#> ── Mean ────────────────────────────────────────────────────────────────────────
#>
#> F1 F2 F3
#> F1 1.000
#> F2 .536 1.000
#> F3 .568 .553 1.000
#>
#> ── Range ───────────────────────────────────────────────────────────────────────
#>
#> F1 F2 F3
#> F1 .000
#> F2 .204 .000
#> F3 .255 .248 .000
#>
#> ══ Variances Accounted for ═════════════════════════════════════════════════════
#>
#> ── Mean ────────────────────────────────────────────────────────────────────────
#>
#> F1 F2 F3
#> SS loadings 2.195 2.074 2.038
#> Prop Tot Var .122 .115 .113
#> Prop Comm Var .348 .329 .323
#>
#> ── Range ───────────────────────────────────────────────────────────────────────
#>
#> F1 F2 F3
#> SS loadings .036 .052 .063
#> Prop Tot Var .002 .003 .003
#> Prop Comm Var .006 .008 .010
#>
#> ══ Model Fit ═══════════════════════════════════════════════════════════════════
#>
#> M (SD) [Min; Max]
#> CAF: .50 (.00) [.50; .50]
#> RMSR: .03 (.00) [.03; .03]
#> SRMR: .02 (.00) [.02; .02]
#> df: 102
# format() returns the same lines as plain text:
writeLines(format(EFA_aver))
#>
#> Averaging performed with averaging method mean (trim = 0) across 72 EFAs,
#> varying the following settings: init_comm, criterion_type, k_promax, p_type,
#> and varimax_type.
#>
#> The error rate is at 0%. Of the solutions that did not result in an error, 100%
#> converged. Of the solutions that converged, 0% contained Heywood cases and 100%
#> were admissible.
#>
#> ══ Indicator-to-Factor Correspondences ═════════════════════════════════════════
#>
#> For each cell, the proportion of solutions including the respective
#> indicator-to-factor correspondence. A salience threshold of 0.3 was used to
#> determine indicator-to-factor correspondences.
#>
#> F1 F2 F3
#> V1 .00 .00 1.00
#> V2 .00 .00 1.00
#> V3 .00 .00 1.00
#> V4 .00 .00 1.00
#> V5 .00 .00 1.00
#> V6 .00 .00 1.00
#> V7 .00 1.00 .00
#> V8 .00 1.00 .00
#> V9 .00 1.00 .00
#> V10 .00 1.00 .00
#> V11 .00 1.00 .00
#> V12 .00 1.00 .00
#> V13 1.00 .00 .00
#> V14 1.00 .00 .00
#> V15 1.00 .00 .00
#> V16 1.00 .00 .00
#> V17 1.00 .00 .00
#> V18 1.00 .00 .00
#>
#> ══ Loadings ════════════════════════════════════════════════════════════════════
#>
#> ── Mean ────────────────────────────────────────────────────────────────────────
#>
#> F1 F2 F3
#> V1 -.019 .056 .588
#> V2 .022 .083 .466
#> V3 .079 .073 .440
#> V4 .119 .017 .532
#> V5 .169 .006 .426
#> V6 -.042 -.023 .672
#> V7 .027 .515 .110
#> V8 .010 .558 .051
#> V9 .060 .530 .022
#> V10 .004 .644 -.043
#> V11 .039 .351 .237
#> V12 .046 .625 .016
#> V13 .590 .105 -.035
#> V14 .527 -.039 .104
#> V15 .540 .143 -.039
#> V16 .534 -.022 .108
#> V17 .634 -.010 .000
#> V18 .535 .029 .071
#>
#> ── Range ───────────────────────────────────────────────────────────────────────
#>
#> F1 F2 F3
#> V1 .098 .077 .092
#> V2 .076 .061 .063
#> V3 .064 .064 .052
#> V4 .069 .087 .069
#> V5 .046 .079 .047
#> V6 .108 .097 .121
#> V7 .059 .043 .052
#> V8 .059 .057 .064
#> V9 .048 .052 .072
#> V10 .059 .079 .090
#> V11 .061 .011 .015
#> V12 .058 .067 .084
#> V13 .072 .044 .099
#> V14 .056 .071 .049
#> V15 .062 .035 .098
#> V16 .055 .071 .052
#> V17 .081 .064 .084
#> V18 .055 .060 .064
#>
#> ══ Factor Intercorrelations from Oblique Solutions ═════════════════════════════
#>
#> ── Mean ────────────────────────────────────────────────────────────────────────
#>
#> F1 F2 F3
#> F1 1.000
#> F2 .536 1.000
#> F3 .568 .553 1.000
#>
#> ── Range ───────────────────────────────────────────────────────────────────────
#>
#> F1 F2 F3
#> F1 .000
#> F2 .204 .000
#> F3 .255 .248 .000
#>
#> ══ Variances Accounted for ═════════════════════════════════════════════════════
#>
#> ── Mean ────────────────────────────────────────────────────────────────────────
#>
#> F1 F2 F3
#> SS loadings 2.195 2.074 2.038
#> Prop Tot Var .122 .115 .113
#> Prop Comm Var .348 .329 .323
#>
#> ── Range ───────────────────────────────────────────────────────────────────────
#>
#> F1 F2 F3
#> SS loadings .036 .052 .063
#> Prop Tot Var .002 .003 .003
#> Prop Comm Var .006 .008 .010
#>
#> ══ Model Fit ═══════════════════════════════════════════════════════════════════
#>
#> M (SD) [Min; Max]
#> CAF: .50 (.00) [.50; .50]
#> RMSR: .03 (.00) [.03; .03]
#> SRMR: .02 (.00) [.02; .02]
#> df: 102
# }