print() shows the Kaiser-Meyer-Olkin (KMO) criterion computed by efa_kmo(): a
titled section with a verdict on the overall KMO value (and what it implies for
the suitability of the data for factor analysis), the overall value, and the
per-variable KMO values. 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_kmo(output fromefa_kmo()).- ...
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
KMO_base <- efa_kmo(test_models$baseline$cormat)
KMO_base
#>
#> ── Kaiser-Meyer-Olkin criterion (KMO) ──────────────────────────────────────────
#>
#> ✔ The overall KMO value for your data is marvellous.
#> These data are probably suitable for factor analysis.
#>
#> Overall: 0.916
#>
#> For each variable:
#> V1 V2 V3 V4 V5 V6 V7 V8 V9 V10 V11 V12 V13
#> 0.900 0.914 0.924 0.932 0.923 0.891 0.928 0.919 0.916 0.892 0.928 0.908 0.922
#> V14 V15 V16 V17 V18
#> 0.905 0.924 0.934 0.907 0.923
# format() returns the same lines as plain text:
writeLines(format(KMO_base))
#>
#> ── Kaiser-Meyer-Olkin criterion (KMO) ──────────────────────────────────────────
#>
#> ✔ The overall KMO value for your data is marvellous.
#> These data are probably suitable for factor analysis.
#>
#> Overall: 0.916
#>
#> For each variable:
#> V1 V2 V3 V4 V5 V6 V7 V8 V9 V10 V11 V12 V13
#> 0.900 0.914 0.924 0.932 0.923 0.891 0.928 0.919 0.916 0.892 0.928 0.908 0.922
#> V14 V15 V16 V17 V18
#> 0.905 0.924 0.934 0.907 0.923