This base::summary() method prints a statistical summary of an ALE
object. If there are no ALE statistics, a message says so. Summarized
statistics are mean or median depending on the boot_centre argument used
for ALE() bootstrapping.
Confidence-region sections are silently omitted when p-values are absent,
including when all_conf = TRUE.
Method arguments
object: An object of classALE.stats: character. One or more values in c("aled", "aler_min", "aler", "aler_max", "naled", "naler_min", "naler", "naler_max"): statistics to report in detail (estimate, p-values, confidence intervals). For others not listed here, only the average (mean or median) estimates are reported. The statistics will be presented in the same order as specified.all_conf: logical(1). By default (FALSE), only statistically significant confidence regions are reported. IfTRUE, all regions are reported as well.round_digits: integer(1). Numbers in tables will be rounded toround_digitsdecimal places.max_rows: natural number. Maximum number of rows to print for any component....: Additional arguments (currently not used).
Examples
# \donttest{
lm_cars <- stats::lm(mpg ~ ., mtcars)
ale_cars <- ALE(lm_cars, boot_it = 3)
summary(ale_cars)
#> <ALE> object of a <lm> model that predicts `mpg` (a numeric outcome) from a 32-row by 11-column dataset.
#> The results were bootstrapped with 3 iterations.
#>
#> Mean ALE statistics [get(object, stats = "estimate")]:
#> # A tibble: 10 × 9
#> term aled aler_min aler aler_max naled naler_min naler naler_max
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 cyl 0.108 -0.237 0.446 0.209 0 0 0 0
#> 2 disp 1.33 -1.98 5.71 3.73 11.3 -17.6 43.1 25.5
#> 3 hp 1.03 -2.90 4.79 1.89 8.85 -22.5 37.3 14.7
#> 4 drat 0.372 -0.630 1.67 1.04 2.48 -8.82 17.6 8.82
#> 5 wt 2.80 -8.83 14.8 5.99 21.0 -46.1 78.4 32.4
#> 6 qsec 1.26 -2.72 7.15 4.43 11.1 -17.6 48.0 30.4
#> 7 vs 0.0199 0.0199 0 0.0199 0 0 0 0
#> 8 am 0.236 -0.0171 0.231 0.214 1.19 0 2.94 2.94
#> 9 gear 0.163 -0.430 1.31 0.881 1.19 0 8.82 8.82
#> 10 carb 0.185 -0.831 1.13 0.299 0.184 -5.88 5.88 0
#>
#> ALE statistic distributions (surrogate p-values, 100 iterations) [get(object, stats = c("aled", "aler", "naled", "naler"))]:
#> # A tibble: 40 × 8
#> statistic term estimate p.value conf.low mean median conf.high
#> <ord> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 aled cyl 0.108 0.89 0.106 0.108 0.108 0.109
#> 2 aled disp 1.33 0.01 1.09 1.33 1.24 1.64
#> 3 aled hp 1.03 0.01 0.835 1.03 1.10 1.17
#> 4 aled drat 0.372 0.4 0.317 0.372 0.376 0.423
#> 5 aled wt 2.80 0 2.16 2.80 2.31 3.88
#> 6 aled qsec 1.26 0.01 0.931 1.26 1.32 1.52
#> 7 aled vs 0.0199 0.98 0.0199 0.0199 0.0199 0.0199
#> 8 aled am 0.236 0.6 0.162 0.236 0.236 0.311
#> 9 aled gear 0.163 0.78 0.145 0.163 0.145 0.198
#> 10 aled carb 0.185 0.74 0.169 0.185 0.184 0.202
#> 11 aler cyl 0.446 0.89 0.446 0.446 0.446 0.446
#> 12 aler disp 5.71 0.01 5.35 5.71 5.35 6.38
#> 13 aler hp 4.79 0.01 4.15 4.79 4.15 5.98
#> 14 aler drat 1.67 0.35 1.45 1.67 1.71 1.85
#> 15 aler wt 14.8 0 14.5 14.8 14.5 15.4
#> 16 aler qsec 7.15 0 6.61 7.15 6.90 7.91
#> 17 aler vs 0 1 0 0 0 0
#> 18 aler am 0.231 0.97 0.0444 0.231 0.306 0.355
#> 19 aler gear 1.31 0.45 1.31 1.31 1.31 1.31
#> 20 aler carb 1.13 0.54 0.638 1.13 1.40 1.40
#> 21 naled cyl 0 0.84 0 0 0 0
#> 22 naled disp 11.3 0 9.27 11.3 10.8 13.8
#> 23 naled hp 8.85 0.01 7.71 8.85 9.28 9.63
#> 24 naled drat 2.48 0.4 1.96 2.48 2.48 3.01
#> 25 naled wt 21.0 0 17.2 21.0 18.3 27.0
#> 26 naled qsec 11.1 0 8.38 11.1 12.1 12.8
#> 27 naled vs 0 0.84 0 0 0 0
#> 28 naled am 1.19 0.72 0 1.19 0 3.41
#> 29 naled gear 1.19 0.72 0.827 1.19 0.827 1.88
#> 30 naled carb 0.184 0.84 0.0138 0.184 0.276 0.276
#> 31 naler cyl 0 0.87 0 0 0 0
#> 32 naler disp 43.1 0 41.2 43.1 41.2 46.8
#> 33 naler hp 37.3 0 32.4 37.3 32.4 46.3
#> 34 naler drat 17.6 0.09 17.6 17.6 17.6 17.6
#> 35 naler wt 78.4 0 76.5 78.4 76.5 82.1
#> 36 naler qsec 48.0 0 47.1 48.0 47.1 49.9
#> 37 naler vs 0 0.87 0 0 0 0
#> 38 naler am 2.94 0.87 0 2.94 0 8.38
#> 39 naler gear 8.82 0.52 8.82 8.82 8.82 8.82
#> 40 naler carb 5.88 0.84 0.441 5.88 8.82 8.82
#>
#> Statistically significant confidence regions [get(object, stats = "conf_sig")]:
#> ! Note that confidence regions are not reliable with fewer than 100 bootstrap
#> iterations or p-values based on fewer than 100 random iterations.
#> ℹ There are 3 bootstrap iterations.
#> ℹ p-values are based on 100 iterations.
#> # A tibble: 10 × 12
#> term x start_x end_x x_span_pct n pct y start_y end_y trend
#> <chr> <chr> <dbl> <dbl> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 disp NA 71.1 351 69.8 26 81.2 NA 17.2 21.2 0.249
#> 2 disp NA 400 472 18.0 6 18.8 NA 21.9 22.8 0.232
#> 3 hp NA 52 205 54.1 26 81.2 NA 21.1 17.8 -0.264
#> 4 hp NA 245 335 31.8 6 18.8 NA 16.9 15.0 -0.264
#> 5 wt NA 1.51 2.32 20.6 7 21.9 NA 25.2 22.2 -0.631
#> 6 wt NA 2.77 3.57 20.5 17 53.1 NA 20.5 17.3 -0.678
#> 7 wt NA 3.78 5.42 42.0 8 25 NA 16.6 10.4 -0.631
#> 8 qsec NA 14.5 14.5 0 1 3.12 NA 16.5 16.5 0
#> 9 qsec NA 15.5 20 53.6 28 87.5 NA 17.2 21.2 0.322
#> 10 qsec NA 22.9 22.9 0 3 9.38 NA 23.6 23.6 0
#> # ℹ 1 more variable: aler_band <ord>
# }