Provides a detailed summary of the CAMEL index computation, including eigenvalues, factor loadings, and weight attribution.
Usage
# S3 method for class 'camel_index'
summary(object, ...)Examples
result <- camel_index(camel_2015, camel_2022)
#> ℹ Using 3 factors (Kaiser criterion suggests 2 for base year).
summary(result)
#>
#> ── CAMEL Index Summary ─────────────────────────────────────────────────────────
#>
#> ── Eigenvalues (Base Year) ──
#>
#> # A tibble: 5 × 3
#> component eigenvalue variance_pct
#> <chr> <dbl> <dbl>
#> 1 PC1 2.16 43.3
#> 2 PC2 1.26 25.1
#> 3 PC3 0.966 19.3
#> 4 PC4 0.324 6.48
#> 5 PC5 0.291 5.83
#> ── Eigenvalues (Current Year) ──
#>
#> # A tibble: 5 × 3
#> component eigenvalue variance_pct
#> <chr> <dbl> <dbl>
#> 1 PC1 2.06 41.1
#> 2 PC2 1.43 28.7
#> 3 PC3 0.788 15.8
#> 4 PC4 0.524 10.5
#> 5 PC5 0.199 3.97
#> ── Factor Loadings (Base Year) ──
#>
#> # A tibble: 5 × 4
#> ratio Factor1 Factor2 Factor3
#> <chr> <dbl> <dbl> <dbl>
#> 1 Ratio1 0.835 0.0532 0.302
#> 2 Ratio2 0.766 0.00678 -0.343
#> 3 Ratio3 -0.162 0.920 0.114
#> 4 Ratio4 -0.00290 0.0102 0.946
#> 5 Ratio5 -0.469 -0.762 0.160
#> ── Index Distribution ──
#>
#> # A tibble: 7 × 3
#> statistic I_mw PD
#> <chr> <dbl> <dbl>
#> 1 Min 3.72 -96.3
#> 2 Q1 99.6 -0.420
#> 3 Median 144. 43.7
#> 4 Mean 160. 60.1
#> 5 Q3 171. 70.9
#> 6 Max 549. 449.
#> 7 SD 115. 115.