Print an efa_sl_loadings object

Usage

# S3 method for class 'efa_sl_loadings'
print(x, ...)

# S3 method for class 'efa_sl_loadings'
format(
  x,
  cutoff = 0.2,
  digits = 3,
  max_name_length = 10,
  color = TRUE,
  name_style = c("truncate", "abbreviate", "full"),
  max_factors_per_block = NULL,
  sort_loadings = c("none", "primary", "clustered"),
  ...
)

Arguments

x: class efa_sl_loadings matrix.
...: additional arguments passed to print or format.
cutoff: numeric. The value at or above which loadings are emphasized (default is .2). The default is lower than the .3 of an ordinary loading table (print.efa_loadings()): the group-factor loadings are residualized, that is, they carry only the variance left once the general factor has been partialled out, and are therefore smaller than the corresponding first-order loadings.
digits: numeric. Passed to round. Number of digits to round the loadings to (default is 3).
max_name_length: numeric. The maximum length of the variable names to display; see print.efa_loadings().
color: logical. Whether to apply console styling using cli. Default is TRUE.
name_style: character. How to shorten variable names longer than max_name_length; see print.efa_loadings().
max_factors_per_block: numeric or NULL. Maximum number of factor columns to print per block. If NULL, the number is chosen from the console width.
sort_loadings: character. Optional row sorting; see print.efa_loadings(). The default "none" keeps the input order. When sorting is requested, rows are grouped by their largest group-factor loading: the general factor is left out of the comparison, since it is the largest loading of almost every item and sorting on it would leave the order untouched.

Value

print() returns its argument x invisibly; it is cat(format(x, ...), sep = "\n") followed by a blank line for console spacing. format() returns a character vector with the table lines (styled to the active console theme; plain when colours are disabled).

Details

Prints a Schmid-Leiman loading matrix (general factor, group factors, and the communality/uniqueness columns) as a styled, decimal-aligned table. Loadings with absolute value greater than or equal to cutoff are emphasised, smaller loadings are de-emphasised, and Heywood-relevant cells (a loading or communality above 1, or a negative uniqueness) are highlighted. If the matrix has many columns or the console is narrow, the table is split into stacked column blocks so the output stays readable.

Examples

EFA_mod <- efa_fit(test_models$baseline$cormat, N = 500, n_factors = 3,
                   estimator = "PAF", rotation = "promax")
efa_schmid_leiman(EFA_mod, estimator = "PAF")
#> 
#> EFA for second-order loadings performed with estimator = 'PAF'
#> 
#> ── Schmid-Leiman Solution ──────────────────────────────────────────────────────
#> 
#>        g     F1     F2     F3    h2    u2
#> V1   .489  -.029   .022   .356  .367  .633
#> V2   .444  -.001   .042   .280  .277  .723
#> V3   .459   .036   .035   .263  .283  .717
#> V4   .522   .061  -.005   .320  .378  .622
#> V5   .468   .095  -.011   .254  .293  .707
#> V6   .478  -.044  -.031   .409  .399  .601
#> V7   .491   .001   .336   .054  .357  .643
#> V8   .463  -.010   .366   .018  .349  .651
#> V9   .457   .023   .347   .000  .330  .670
#> V10  .449  -.013   .425  -.041  .383  .617
#> V11  .477   .009   .224   .135  .297  .703
#> V12  .513   .012   .410  -.006  .432  .568
#> V13  .502   .372   .054  -.039  .395  .605
#> V14  .455   .332  -.043   .051  .322  .678
#> V15  .489   .340   .081  -.041  .363  .637
#> V16  .476   .336  -.032   .053  .343  .657
#> V17  .477   .402  -.023  -.016  .390  .610
#> V18  .485   .336   .003   .029  .350  .650
#> 
#> ── Variances Accounted for ─────────────────────────────────────────────────────
#> 
#>                      g     F1    F2     F3
#> SS loadings        4.111  .770  .783   .642
#> Prop Tot Var        .228  .043  .044   .036
#> Cum Prop Tot Var    .228  .271  .315   .350
#> Prop Comm Var       .652  .122  .124   .102
#> Cum Prop Comm Var   .652  .774  .898  1.000