This base::summary() method prints a statistical summary of a ModelBoot
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 classModelBoot.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 <- ModelBoot(lm_cars, boot_it = 3)
summary(ale_cars)
#> <ModelBoot> object of a <lm> model that predicts `mpg` (a numeric outcome) from a 32-row by 11-column dataset.
#> * The model was retrained with 3 bootstrap iterations.
#>
#> Overall model statistics (object@model_stats):
#> # A tibble: 12 × 7
#> name boot_valid conf.low median mean conf.high sd
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 r.squared NA 0.906 0.931 0.930 0.953 0.0249
#> 2 adj.r.squared NA 0.861 0.898 0.897 0.931 0.0367
#> 3 sigma NA 1.68 1.70 1.75 1.85 0.0984
#> 4 statistic NA 20.3 28.3 30.7 43.0 12.1
#> 5 p.value NA 0 0 0 0 0
#> 6 df NA 10 10 10 10 0
#> 7 df.residual NA 21 21 21 21 0
#> 8 nobs NA 32 32 32 32 0
#> 9 mae 3.27 3.13 NA NA 4.76 0.981
#> 10 sa_mae 0.629 0.410 NA NA 0.705 0.173
#> 11 rmse 4.27 3.48 NA NA 7.10 1.96
#> 12 sa_rmse 0.625 0.326 NA NA 0.738 0.222
#>
#> Summary model term estimates (object@model_coefs):
#> # A tibble: 11 × 6
#> term conf.low median mean conf.high std.error
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 (Intercept) -99.5 -44.3 -45.3 8.00 56.6
#> 2 cyl -1.03 0.593 0.281 1.33 1.28
#> 3 disp 0.0099 0.0161 0.0165 0.0235 0.0072
#> 4 hp -0.0345 0.0359 0.0205 0.0625 0.0528
#> 5 drat 1.92 4.30 4.03 5.91 2.11
#> 6 wt -5.46 -4.34 -4.16 -2.71 1.45
#> 7 qsec 0.569 2.91 2.58 4.32 2.00
#> 8 vs -7.40 -6.84 -4.00 1.81 5.44
#> 9 am -2.68 0.298 0.402 3.57 3.29
#> 10 gear 0.580 4.54 4.59 8.66 4.25
#> 11 carb -2.27 -2.00 -1.74 -1.000 0.705
#>
#> Mean ALE statistics [get(object, stats = "estimate")]:
#> # A tibble: 10 × 9
#> term aled aler_min aler aler_max naled naler_min naler naler_max
#> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 cyl 1.01 -1.94 4.11 2.18 15.5 -20.7 34.2 13.5
#> 2 disp 1.54 -2.58 6.53 3.95 18.0 -32.0 63.6 31.6
#> 3 hp 2.09 -5.06 11.1 6.05 16.8 -40.7 75.5 34.7
#> 4 drat 1.70 -2.58 8.54 5.95 14.1 -24.1 57.0 32.9
#> 5 wt 2.59 -8.95 15.8 6.87 18.7 -49.1 87.5 38.4
#> 6 qsec 3.19 -7.52 13.7 6.20 22.3 -41.2 75.0 33.8
#> 7 vs 2.69 -2.80 5.51 2.71 24.1 -33.5 48.0 14.5
#> 8 am 0.912 -0.987 2.29 1.31 17.1 -16.5 39.1 22.6
#> 9 gear 0.453 -2.54 9.19 6.65 1.05 -18.5 44.8 26.4
#> 10 carb 1.53 -7.19 9.42 2.24 15.2 -39.6 53.2 13.5
#>
#> 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 median mean conf.high
#> <ord> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 aled cyl 1.01 0.01 0.617 1.12 1.01 1.31
#> 2 aled disp 1.54 0.01 0.978 1.54 1.54 2.12
#> 3 aled hp 2.09 0 1.53 1.87 2.09 2.83
#> 4 aled drat 1.70 0.01 0.569 1.89 1.70 2.66
#> 5 aled wt 2.59 0 1.28 2.39 2.59 4.08
#> 6 aled qsec 3.19 0 0.790 3.66 3.19 5.20
#> 7 aled vs 2.69 0 1.17 3.30 2.69 3.68
#> 8 aled am 0.912 0.05 0.167 1.22 0.912 1.40
#> 9 aled gear 0.453 0.34 0.0551 0.275 0.453 1.00
#> 10 aled carb 1.53 0.01 0.875 1.76 1.53 2.00
#> 11 aler cyl 4.11 0.02 2.48 4.48 4.11 5.44
#> 12 aler disp 6.53 0 3.98 6.44 6.53 9.16
#> 13 aler hp 11.1 0 10.2 10.8 11.1 12.3
#> 14 aler drat 8.54 0 3.59 9.32 8.54 12.8
#> 15 aler wt 15.8 0 10.6 16.5 15.8 20.4
#> 16 aler qsec 13.7 0 4.29 16.4 13.7 20.9
#> 17 aler vs 5.51 0.01 2.50 6.84 5.51 7.40
#> 18 aler am 2.29 0.28 0.424 2.84 2.29 3.7
#> 19 aler gear 9.19 0 1.16 9.08 9.19 17.3
#> 20 aler carb 9.42 0 3.27 11.4 9.42 13.9
#> 21 naled cyl 15.5 0 9.47 11.2 15.5 25.3
#> 22 naled disp 18.0 0 13.7 20.2 18.0 20.3
#> 23 naled hp 16.8 0 9.99 15.4 16.8 24.7
#> 24 naled drat 14.1 0 4.38 12.6 14.1 25.2
#> 25 naled wt 18.7 0 11.2 15.3 18.7 29.0
#> 26 naled qsec 22.3 0 8.70 23.9 22.3 34.5
#> 27 naled vs 24.1 0 13.9 27.1 24.1 31.7
#> 28 naled am 17.1 0 3.44 24.2 17.1 24.8
#> 29 naled gear 1.05 0.72 0.0439 0.879 1.05 2.19
#> 30 naled carb 15.2 0 8.17 13.5 15.2 23.7
#> 31 naler cyl 34.2 0.01 21.4 28.1 34.2 52.3
#> 32 naler disp 63.6 0 54.2 65.4 63.6 71.5
#> 33 naler hp 75.5 0 71.1 71.9 75.5 82.9
#> 34 naler drat 57.0 0 46.4 53.1 57.0 70.9
#> 35 naler wt 87.5 0 86.2 87.5 87.5 89.0
#> 36 naler qsec 75.0 0 53.2 86.1 75.0 87.4
#> 37 naler vs 48.0 0 23.0 59.4 48.0 63.3
#> 38 naler am 39.1 0 11.4 50.8 39.1 56.8
#> 39 naler gear 44.8 0 2.5 50 44.8 82.8
#> 40 naler carb 53.2 0 25.8 65.6 53.2 70.0
#>
#> 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: 15 × 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 71.1 0 1 3.12 NA 16.6 16.6 0
#> 2 disp NA 79 79 0 3 9.38 NA 16.8 16.8 0
#> 3 disp NA 120. 304 45.9 19 59.4 NA 17.5 20.5 0.280
#> 4 disp NA 351 351 0 3 9.38 NA 21.2 21.2 0
#> 5 disp NA 400 400 0 3 9.38 NA 22.1 22.1 0
#> 6 disp NA 472 472 0 3 9.38 NA 22.4 22.4 0
#> 7 drat NA 2.76 4.22 67.3 30 93.8 NA 16.6 22.1 0.352
#> 8 drat NA 4.93 4.93 0 2 6.25 NA 25.2 25.2 0
#> 9 wt NA 1.51 2.32 20.6 7 21.9 NA 25.8 21.4 -0.924
#> 10 wt NA 2.77 3.78 25.8 19 59.4 NA 20.7 17.4 -0.557
#> 11 wt NA 4.07 5.42 34.6 6 18.8 NA 15.4 11.7 -0.470
#> 12 qsec NA 14.5 20 65.5 29 90.6 NA 17.8 22.7 0.327
#> 13 qsec NA 22.9 22.9 0 3 9.38 NA 21.4 21.4 0
#> 14 carb NA 1 4 42.9 30 93.8 NA 21.4 16.2 -0.530
#> 15 carb NA 8 8 0 2 6.25 NA 7.84 7.84 0
#> # ℹ 1 more variable: aler_band <ord>
# }