print() shows a summarised output of the efa_compare() function: the mean
(with its range), median, and root mean squared distance (RMSE) of the
differences, the number of decimals to which all numbers agree, the minimum
number of decimals provided, and (for matrices) the number of differing
indicator-to-factor correspondences, followed (optionally) by the table of
elementwise differences. 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_compare(output fromefa_compare()).- ...
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
# A type SPSS EFA to mimick the SPSS implementation
EFA_SPSS_5 <- efa_fit(IDS2_R, n_factors = 5,
estimate_control = estimate_control(type = "SPSS"),
rotate_control = rotate_control(type = "SPSS"))
#> Warning: Reached the maximum number of iterations without convergence; results may not
#> be interpretable.
# A type psych EFA to mimick the psych::fa() implementation
EFA_psych_5 <- efa_fit(IDS2_R, n_factors = 5,
estimate_control = estimate_control(type = "psych"),
rotate_control = rotate_control(type = "psych"))
# compare the two
comp <- efa_compare(EFA_SPSS_5$unrot_loadings, EFA_psych_5$unrot_loadings,
x_labels = c("SPSS", "psych"))
comp
#> Mean [min, max] absolute difference: 0.0017 [ 0.0000, 0.0090]
#> Median absolute difference: 0.0009
#> Root mean squared distance (RMSE): 0.0025
#> Max decimals where all numbers agree in absolute value: 1
#> Minimum number of decimals provided: 17
#> Differing indicator-to-factor correspondences: 0 (highest loading), 0 (all |loadings| >= 0.3)
#>
#> F1 F2 F3 F4 F5
#> GS .0004 .0002 -.0002 .0002 -.0034
#> PL .0000 .0001 .0007 -.0003 .0001
#> TC .0007 .0021 -.0058 .0001 .0024
#> CB -.0007 -.0039 -.0005 .0043 -.0024
#> NL .0020 -.0090 -.0056 .0032 .0016
#> NLM -.0018 .0069 .0082 .0034 -.0011
#> GF -.0002 .0003 .0036 -.0021 -.0003
#> RGF .0004 .0011 .0049 .0020 .0011
#> CM -.0001 -.0001 .0004 -.0009 .0011
#> EP -.0001 -.0001 .0003 -.0010 .0009
#> CA -.0001 .0006 -.0020 -.0027 .0009
#> OP -.0001 .0007 -.0016 -.0025 .0008
#> RS -.0001 .0012 -.0013 -.0032 -.0010
#> DP -.0001 .0009 .0004 -.0024 -.0009
# format() returns the same lines as plain text:
writeLines(format(comp))
#> Mean [min, max] absolute difference: 0.0017 [ 0.0000, 0.0090]
#> Median absolute difference: 0.0009
#> Root mean squared distance (RMSE): 0.0025
#> Max decimals where all numbers agree in absolute value: 1
#> Minimum number of decimals provided: 17
#> Differing indicator-to-factor correspondences: 0 (highest loading), 0 (all |loadings| >= 0.3)
#>
#> F1 F2 F3 F4 F5
#> GS .0004 .0002 -.0002 .0002 -.0034
#> PL .0000 .0001 .0007 -.0003 .0001
#> TC .0007 .0021 -.0058 .0001 .0024
#> CB -.0007 -.0039 -.0005 .0043 -.0024
#> NL .0020 -.0090 -.0056 .0032 .0016
#> NLM -.0018 .0069 .0082 .0034 -.0011
#> GF -.0002 .0003 .0036 -.0021 -.0003
#> RGF .0004 .0011 .0049 .0020 .0011
#> CM -.0001 -.0001 .0004 -.0009 .0011
#> EP -.0001 -.0001 .0003 -.0010 .0009
#> CA -.0001 .0006 -.0020 -.0027 .0009
#> OP -.0001 .0007 -.0016 -.0025 .0008
#> RS -.0001 .0012 -.0013 -.0032 -.0010
#> DP -.0001 .0009 .0004 -.0024 -.0009