diff --git a/DESCRIPTION b/DESCRIPTION index 202a1bf1..70e44dbb 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,6 +1,6 @@ Package: ingredients Title: Effects and Importances of Model Ingredients -Version: 0.3.2 +Version: 0.3.3 Authors@R: c(person("Przemyslaw", "Biecek", email = "przemyslaw.biecek@gmail.com", role = c("aut", "cre"), comment = c(ORCID = "0000-0001-8423-1823")), diff --git a/NAMESPACE b/NAMESPACE index c2df90fc..6c1e6de4 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -44,7 +44,7 @@ export(partial_dependency) export(plotD3) export(select_neighbours) export(select_sample) -export(show_aggreagated_profiles) +export(show_aggregated_profiles) export(show_observations) export(show_profiles) export(show_residuals) diff --git a/NEWS.md b/NEWS.md index 15619d35..8283b44a 100644 --- a/NEWS.md +++ b/NEWS.md @@ -1,3 +1,9 @@ +ingredients 0.3.3 +---------------------------------------------------------------- +* `show_profiles` and `show_residuals` functions extend Ceteris Paribus Plots. +* `show_aggreagated_profiles` is renamed to `show_aggregated_profiles` +* centering of ggplot2 title + ingredients 0.3.2 ---------------------------------------------------------------- * added new functions `describe()` and `print.ceteris_paribus_descriptions()` for text based descriptions of Ceteris Paribus explainers diff --git a/R/aggreagate_profiles.R b/R/aggreagate_profiles.R index a2e475aa..9c2a9b56 100644 --- a/R/aggreagate_profiles.R +++ b/R/aggreagate_profiles.R @@ -46,7 +46,7 @@ #' plot(cp_rf, variables = "age") + #' show_observations(cp_rf, variables = "age") + #' show_rugs(cp_rf, variables = "age", color = "red") + -#' show_aggreagated_profiles(pdp_rf, size = 3, color = "_label_") +#' show_aggregated_profiles(pdp_rf, size = 3, color = "_label_") #' } #' @export aggregate_profiles <- function(x, ..., diff --git a/R/cluster_profiles.R b/R/cluster_profiles.R index 81ff7ea6..734a3031 100644 --- a/R/cluster_profiles.R +++ b/R/cluster_profiles.R @@ -51,10 +51,10 @@ #' head(clust_rf) #' #' plot(clust_rf, color = "_label_") + -#' show_aggreagated_profiles(pdp_rf, color = "black", size = 3) +#' show_aggregated_profiles(pdp_rf, color = "black", size = 3) #' #' plot(cp_rf, color = "grey", variables = "age") + -#' show_aggreagated_profiles(clust_rf, color = "_label_", size = 2) +#' show_aggregated_profiles(clust_rf, color = "_label_", size = 2) #' #' clust_rf <- cluster_profiles(cp_rf, k = 3, center = TRUE, variables = "age") #' head(clust_rf) diff --git a/R/plot_aggregated_ceteris_paribus_explainer.R b/R/plot_aggregated_ceteris_paribus_explainer.R index d7341aae..9e02c0bc 100644 --- a/R/plot_aggregated_ceteris_paribus_explainer.R +++ b/R/plot_aggregated_ceteris_paribus_explainer.R @@ -1,6 +1,6 @@ #' Adds a Layer with Aggregated Profiles #' -#' Function 'show_aggreagated_profiles' adds a layer to a plot created with 'plot.ceteris_paribus_explainer'. +#' Function 'show_aggregated_profiles' adds a layer to a plot created with 'plot.ceteris_paribus_explainer'. #' #' @param x a ceteris paribus explainer produced with function `ceteris_paribus()` #' @param ... other explainers that shall be plotted together @@ -62,7 +62,7 @@ #' plot(cp_rf, variables = "age") + #' show_observations(cp_rf, variables = "age") + #' show_rugs(cp_rf, variables = "age", color = "red") + -#' show_aggreagated_profiles(pdp_rf_p, size = 2) +#' show_aggregated_profiles(pdp_rf_p, size = 2) #' #' } #' @export diff --git a/R/show_aggregated_profiles.R b/R/show_aggregated_profiles.R index 0893d87c..ddc4f1c6 100644 --- a/R/show_aggregated_profiles.R +++ b/R/show_aggregated_profiles.R @@ -1,6 +1,6 @@ #' Adds a Layer with Aggregated Profiles #' -#' Function 'show_aggreagated_profiles' adds a layer to a plot created with 'plot.ceteris_paribus_explainer'. +#' Function 'show_aggregated_profiles' adds a layer to a plot created with 'plot.ceteris_paribus_explainer'. #' #' @param x a ceteris paribus explainer produced with function `ceteris_paribus()` #' @param ... other explainers that shall be plotted together @@ -27,7 +27,7 @@ #' pdp_rf <- aggregate_profiles(cp_rf, variables = "age") #' plot(cp_rf, variables = "age") + #' show_observations(cp_rf, variables = "age") + -#' show_aggreagated_profiles(pdp_rf, size = 3) +#' show_aggregated_profiles(pdp_rf, size = 3) #' #' \donttest{ #' library("randomForest") @@ -48,13 +48,13 @@ #' plot(cp_rf, variables = "age") + #' show_observations(cp_rf, variables = "age") + #' show_rugs(cp_rf, variables = "age", color = "red") + -#' show_aggreagated_profiles(pdp_rf, size = 3) +#' show_aggregated_profiles(pdp_rf, size = 3) #' #' plot(pdp_rf, variables = "age", color = "grey") #' #' } #' @export -show_aggreagated_profiles <- function(x, ..., +show_aggregated_profiles <- function(x, ..., size = 0.5, alpha = 1, color = "#371ea3", diff --git a/R/show_rugs.R b/R/show_rugs.R index b7b13f4d..880640fd 100644 --- a/R/show_rugs.R +++ b/R/show_rugs.R @@ -30,7 +30,7 @@ #' pdp_rf <- aggregate_profiles(cp_rf, variables = "age") #' plot(cp_rf, variables = "age") + #' show_observations(cp_rf, variables = "age") + -#' show_aggreagated_profiles(pdp_rf, size = 3) +#' show_aggregated_profiles(pdp_rf, size = 3) #' #' \donttest{ #' library("randomForest") @@ -51,7 +51,7 @@ #' plot(cp_rf, variables = "age") + #' show_observations(cp_rf, variables = "age") + #' show_rugs(cp_rf, variables = "age", color = "red") + -#' show_aggreagated_profiles(pdp_rf, size = 3) +#' show_aggregated_profiles(pdp_rf, size = 3) #' #' plot(pdp_rf, variables = "age", color = "grey") #' diff --git a/R/theme_drwhy.R b/R/theme_drwhy.R index 6c6cc1d1..54cf9a48 100644 --- a/R/theme_drwhy.R +++ b/R/theme_drwhy.R @@ -15,8 +15,8 @@ theme_drwhy <- function() { axis.line.y = element_line(color = "white"), axis.ticks.y = element_line(color = "white"), #axis.line = element_line(color = "#371ea3", size = 0.5, linetype = 1), - plot.title = element_text(color = "#371ea3", size = 16), - plot.subtitle = element_text(color = "#371ea3", size = 14), + plot.title = element_text(color = "#371ea3", size = 16, hjust = 0), + plot.subtitle = element_text(color = "#371ea3", size = 14, hjust = 0), axis.title = element_text(color = "#371ea3"), axis.text = element_text(color = "#371ea3", size = 10), strip.text = element_text(color = "#371ea3", size = 12, hjust = 0, margin = margin(0, 0, 1, 0)), @@ -36,8 +36,8 @@ theme_drwhy_vertical <- function() { legend.direction = "horizontal", legend.position = "top", axis.line.x = element_line(color = "white"), axis.ticks.x = element_line(color = "white"), - plot.title = element_text(color = "#371ea3", size = 16), - plot.subtitle = element_text(color = "#371ea3", size = 14), + plot.title = element_text(color = "#371ea3", size = 16, hjust = 0), + plot.subtitle = element_text(color = "#371ea3", size = 14, hjust = 0), #axis.line = element_line(color = "#371ea3", size = 0.5, linetype = 1), axis.title = element_text(color = "#371ea3"), axis.text = element_text(color = "#371ea3", size = 10), @@ -56,8 +56,8 @@ theme_drwhy_blank <- function() { panel.border = element_blank(), strip.background = element_blank(), plot.background = element_blank(), complete = TRUE, legend.direction = "horizontal", legend.position = "top", - plot.title = element_text(color = "#371ea3", size = 16), - plot.subtitle = element_text(color = "#371ea3", size = 14), + plot.title = element_text(color = "#371ea3", size = 16, hjust = 0), + plot.subtitle = element_text(color = "#371ea3", size = 14, hjust = 0), axis.line.x = element_line(color = "white"), axis.ticks.x = element_line(color = "white"), axis.title = element_text(color = "#371ea3"), diff --git a/docs/articles/vignette_titanic.html b/docs/articles/vignette_titanic.html index 76a34f72..c9f330bd 100644 --- a/docs/articles/vignette_titanic.html +++ b/docs/articles/vignette_titanic.html @@ -76,7 +76,7 @@

Survival on the RMS Titanic

Przemyslaw Biecek

-

2019-04-14

+

2019-04-24

@@ -116,8 +116,8 @@

#> Number of trees: 500 #> No. of variables tried at each split: 2 #> -#> Mean of squared residuals: 0.143218 -#> % Var explained: 34.66 +#> Mean of squared residuals: 0.1428399 +#> % Var explained: 34.83

@@ -138,18 +138,18 @@

fi_rf <- feature_importance(explain_titanic_rf) head(fi_rf)

#>       variable dropout_loss            label
-#> 1 _full_model_    0.3338510 Random Forest v7
-#> 2      country    0.3338510 Random Forest v7
-#> 3        parch    0.3447046 Random Forest v7
-#> 4        sibsp    0.3452359 Random Forest v7
-#> 5     embarked    0.3507052 Random Forest v7
-#> 6         fare    0.3740003 Random Forest v7
+#> 1 _full_model_ 0.3329331 Random Forest v7 +#> 2 country 0.3329331 Random Forest v7 +#> 3 parch 0.3441589 Random Forest v7 +#> 4 sibsp 0.3452181 Random Forest v7 +#> 5 embarked 0.3508888 Random Forest v7 +#> 6 fare 0.3744391 Random Forest v7 
plot(fi_rf)

library("r2d3")
 plotD3(fi_rf)
-
- +
+

@@ -166,12 +166,12 @@ 

pp_age  <- partial_dependency(explain_titanic_rf, variables =  c("age", "fare"))
 head(pp_age)
#>   _vname_          _label_       _x_    _yhat_ _ids_
-#> 1    fare Random Forest v7 0.0000000 0.3419235     0
-#> 2     age Random Forest v7 0.1666667 0.5430471     0
-#> 3     age Random Forest v7 2.0000000 0.5745798     0
-#> 4     age Random Forest v7 4.0000000 0.5812799     0
-#> 5    fare Random Forest v7 6.1904000 0.3271178     0
-#> 6     age Random Forest v7 7.0000000 0.5388531     0
+#> 1 fare Random Forest v7 0.0000000 0.3060236 0 +#> 2 age Random Forest v7 0.1666667 0.5124979 0 +#> 3 age Random Forest v7 2.0000000 0.5525708 0 +#> 4 age Random Forest v7 4.0000000 0.5472667 0 +#> 5 fare Random Forest v7 6.1904000 0.2901396 0 +#> 6 age Random Forest v7 7.0000000 0.5094431 0

@@ -226,14 +226,14 @@

clust_rf <- cluster_profiles(sp_rf, k = 3) head(clust_rf) 
#>   _vname_            _label_       _x_ _cluster_    _yhat_ _ids_
-#> 1    fare Random Forest v7_1 0.0000000         1 0.7424063     0
-#> 2   sibsp Random Forest v7_1 0.0000000         1 0.8697368     0
-#> 3   parch Random Forest v7_1 0.0000000         1 0.8240121     0
-#> 4     age Random Forest v7_1 0.1666667         1 0.8003449     0
-#> 5   parch Random Forest v7_1 0.2800000         1 0.8242621     0
-#> 6   sibsp Random Forest v7_1 1.0000000         1 0.8182423     0
+#> 1 fare Random Forest v7_1 0.0000000 1 0.7411514 0 +#> 2 sibsp Random Forest v7_1 0.0000000 1 0.8946084 0 +#> 3 parch Random Forest v7_1 0.0000000 1 0.8490757 0 +#> 4 age Random Forest v7_1 0.1666667 1 0.8119124 0 +#> 5 parch Random Forest v7_1 0.2800000 1 0.8493557 0 +#> 6 sibsp Random Forest v7_1 1.0000000 1 0.8359760 0 
plot(sp_rf, alpha = 0.1) +
-  show_aggreagated_profiles(clust_rf, color = "_label_", size = 2)
+ show_aggregated_profiles(clust_rf, color = "_label_", size = 2)

@@ -255,7 +255,7 @@

#> [1] stats graphics grDevices utils datasets methods base #> #> other attached packages: -#> [1] r2d3_0.2.3 ingredients_0.3.2 randomForest_4.6-14 +#> [1] r2d3_0.2.3 ingredients_0.3.3 randomForest_4.6-14 #> [4] DALEX_0.3.1 #> #> loaded via a namespace (and not attached): @@ -263,7 +263,7 @@

#> [5] tools_3.5.0 digest_0.6.18 jsonlite_1.6 evaluate_0.13 #> [9] memoise_1.1.0 tibble_2.1.1 gtable_0.3.0 pkgconfig_2.0.2 #> [13] rlang_0.3.4 rstudioapi_0.10 commonmark_1.5 yaml_2.2.0 -#> [17] pkgdown_1.0.0 xfun_0.5 stringr_1.4.0 dplyr_0.8.0.1 +#> [17] pkgdown_1.0.0 xfun_0.6 stringr_1.4.0 dplyr_0.8.0.1 #> [21] roxygen2_6.1.1 xml2_1.2.0 knitr_1.22 htmlwidgets_1.3 #> [25] desc_1.2.0 fs_1.2.6 rprojroot_1.3-2 grid_3.5.0 #> [29] tidyselect_0.2.5 glue_1.3.1 R6_2.4.0 rmarkdown_1.12 diff --git a/docs/articles/vignette_titanic_files/figure-html/unnamed-chunk-10-1.png b/docs/articles/vignette_titanic_files/figure-html/unnamed-chunk-10-1.png index 3fd3c71a..ad0c4537 100644 Binary files a/docs/articles/vignette_titanic_files/figure-html/unnamed-chunk-10-1.png and b/docs/articles/vignette_titanic_files/figure-html/unnamed-chunk-10-1.png differ diff --git a/docs/articles/vignette_titanic_files/figure-html/unnamed-chunk-4-1.png b/docs/articles/vignette_titanic_files/figure-html/unnamed-chunk-4-1.png index 5bd9db7d..b1c9cf18 100644 Binary files a/docs/articles/vignette_titanic_files/figure-html/unnamed-chunk-4-1.png and b/docs/articles/vignette_titanic_files/figure-html/unnamed-chunk-4-1.png differ diff --git a/docs/articles/vignette_titanic_files/figure-html/unnamed-chunk-5-1.png b/docs/articles/vignette_titanic_files/figure-html/unnamed-chunk-5-1.png index ab6eeead..923ce464 100644 Binary files a/docs/articles/vignette_titanic_files/figure-html/unnamed-chunk-5-1.png and b/docs/articles/vignette_titanic_files/figure-html/unnamed-chunk-5-1.png differ diff --git a/docs/articles/vignette_titanic_files/figure-html/unnamed-chunk-6-1.png b/docs/articles/vignette_titanic_files/figure-html/unnamed-chunk-6-1.png index e879de67..dec8dabc 100644 Binary files a/docs/articles/vignette_titanic_files/figure-html/unnamed-chunk-6-1.png and b/docs/articles/vignette_titanic_files/figure-html/unnamed-chunk-6-1.png differ diff --git a/docs/articles/vignette_titanic_files/figure-html/unnamed-chunk-7-1.png b/docs/articles/vignette_titanic_files/figure-html/unnamed-chunk-7-1.png index a540255a..6e374175 100644 Binary files a/docs/articles/vignette_titanic_files/figure-html/unnamed-chunk-7-1.png and b/docs/articles/vignette_titanic_files/figure-html/unnamed-chunk-7-1.png differ diff --git a/docs/articles/vignette_titanic_files/figure-html/unnamed-chunk-8-1.png b/docs/articles/vignette_titanic_files/figure-html/unnamed-chunk-8-1.png index 01755e0e..99760d7f 100644 Binary files a/docs/articles/vignette_titanic_files/figure-html/unnamed-chunk-8-1.png and b/docs/articles/vignette_titanic_files/figure-html/unnamed-chunk-8-1.png differ diff --git a/docs/articles/vignette_titanic_files/figure-html/unnamed-chunk-9-1.png b/docs/articles/vignette_titanic_files/figure-html/unnamed-chunk-9-1.png index ad8314a4..eeffe1c4 100644 Binary files a/docs/articles/vignette_titanic_files/figure-html/unnamed-chunk-9-1.png and b/docs/articles/vignette_titanic_files/figure-html/unnamed-chunk-9-1.png differ diff --git a/docs/news/index.html b/docs/news/index.html index cc0ef6d6..4be7a9b2 100644 --- a/docs/news/index.html +++ b/docs/news/index.html @@ -105,6 +105,17 @@

Change log

+
+

+ingredients 0.3.3

+
    +
  • +show_profiles and show_residuals functions extend Ceteris Paribus Plots.
    • +
  • +show_aggreagated_profiles is renamed to show_aggregated_profiles +
    • +
+

ingredients 0.3.2

@@ -163,6 +174,7 @@

Contents

    +
  • 0.3.3
  • 0.3.2
  • 0.3.1
  • 0.3
    • diff --git a/docs/reference/aggregate_profiles.html b/docs/reference/aggregate_profiles.html index 066e2a7a..859c106f 100644 --- a/docs/reference/aggregate_profiles.html +++ b/docs/reference/aggregate_profiles.html @@ -154,76 +154,18 @@

    R

    Examples

    library("DALEX")
    library("randomForest")
  model_titanic_rf <- randomForest(survived ~ gender + age + class + embarked +
-                                    fare + sibsp + parch,  data = titanic)
- model_titanic_rf
    #> 
-#> Call:
-#>  randomForest(formula = survived ~ gender + age + class + embarked +      fare + sibsp + parch, data = titanic) 
-#>                Type of random forest: classification
-#>                      Number of trees: 500
-#> No. of variables tried at each split: 2
-#> 
-#>         OOB estimate of  error rate: 19.25%
-#> Confusion matrix:
-#>       no yes class.error
-#> no  1318 100  0.07052186
-#> yes  304 377  0.44640235
    
+                                    fare + sibsp + parch,  data = titanic)
    #> Error in na.fail.default(structure(list(survived = structure(c(1L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 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2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 1L), .Label = c("no", "yes"), class = "factor"), gender = structure(c(2L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 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2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("female", "male"), class = "factor"), age = c(42, 13, 16, 39, 16, 25, 30, 28, 27, 20, 30, 27, 40, 0.8333333333, 18, 35, 25, 19, 24, 25, 29, 39, 0.9166666667, 2, 30, 25, 18, 19, 19, 33, 47, 4, 6, 2, 16, 38, 9, 11, 39, 26, 39, 20, 17, 27, 62, 39, 26, 36, 31, 53, 25, 18, 25, 71, 57, 35, 5, 9, 3, 13, 5, 40, 13, 38, 45, 23, 47, 18, 17, 27, 24, 23, 15, 32, 33, 3, 0.75, 5, 23, 18, 40, 18, 22, 26, 36, 28, 31, 18, 45, 27, 47, 33, 22, 32, 52, 23, 60, 24, 50, 32, 22, 36, 44, 20, 1, 4, 12, 35, 37, 46, 34, 26, 26, 19, 22, 23, 39, 46, 32, 31, 21, 24, 25, 19, 18, 28, 45, 39, 30, 61, 41, 26, 25, 40, 42, 32, 45, 26, 42, 22, 30, 22, 41, 20, 24, 48, 29, 23, 37, 46, 20, 32, 22, 40, 18, 15, 59, 40, 44, 60, 20, 25, 22, 21, 58, 7, 10, 40, 19, 41, 17, 36, 26, 21, 46, 42, 40, 21, 30, 18, 38, 28, 42, 0.8333333333, 26, 28, 17, 17, 35, 21, 22, 21, 52, 29, 28, 18, 19, 58, 36, 28, 24, 33, 17, 27, 45, 54, 11, 13, 45, 36, 36, 28, 49, 36, 25, 36, 24, 46, 47, 27, 32, 52, 37, 29, 20, 36, 30, 45, 48, 32, 43, 29, 25, 52, 26, 21, 29, 28, 28, 26, 27, 22, 40, 20, 18, 24, 24, 29, 27, 25, 37, 8, 31, 30, 36, 39, 37, 64, 31, 40, 23, 41, 66, 43, 22, 30, 32, 35, 27, 19, 30, 55, 16, 21, 3, 9, 36, 58, 59, 20, 16, 43, 70, 39, 64, 17, 39, 35, 22, 28, 22, 45, 19, 33, 29, 51, 0.3333333333, 34, 28, 27, 36, 27, 27, 31, 8, 24, 19, 22, 22, 17, 48, 28, 34, 32, 18, 1, 0.1666666667, 25, 32, 25, 24, 29, 36, 36, 30, 25, 30, 22, 26, 16, 19, 18, 31, 17, 17, 42, 43, 52, 4, 34, 18, 34, 21, 37, 18, 22, 48, 27, 50, 55, 31, 28, 33, 8, 42, 34, 27, 24, 48, 49, 39, 24, 27, 30, 65, 23, 22, 23, 18, 25, 16, 45, 29, 15, 22, 48, 5, 21, 39, 37, 54, 36, 38, 18, 46, 26, 49, 32, 18, 34, 18, 48, 22, 42, 28, 48, 36, 19, 20, 32, 20, 7, 23, 18, 54, 14, 30, 24, 28, 23, 19, 64, 60, 28, 38, 32, 37, 49, 43, 42, 22, 60, 48, 39, 39, 38, 37, 35, 36, 38, 33, 29, 23, 39, 19, 26, 47, 38, 21, NA, 22, 44, 35, 23, 47, 19, 21, 24, 23, 31, 24, 17, 30, 18, 47, 40, 71, 9, 33, 41, 31, 9, 1, 11, 10, 16, 14, 42, 43, 53, 19, 38, 59, 52, 45, 23, 20, 41, 32, 25, 46, 19, 37, 28, 19, 24, 28, 19, 28, 24, 30, 0.5833333333, 23, 19, 27, 18, 35, 41, 26, 21, 45, 45, 25, 21, 17, 21, 6, 48, 49, 39, 37, 62, 45, 35, 45, 46, 33, 7, 47, 29, 48, 27, 45, 24, 55, 52, 42, 33, 27, 24, 20, 16, 23, 22, 30, 28, 21, 24, 24, 50, 50, 56, 24, 30, 21, 44, 18, 55, 17, 44, 2, 22, 20, 24, 36, 54, 50, 51, 29, 44, 43, 18, 42, 35, 40, 27, 21, 22, 41, 26, 63, 61, 38, 31, 43, 42, 21, 34, 34, 11, 18, 33, 39, 17, 32, 17, 25, 50, 49, 20, 16, 19, 24, 42, 30, 21, 21, 46, 24, 21, 32, 20, 48, 17, 26, 23, 29, 34, 22, 33, 31, 29, 26, 4, 1, 49, 33, 26, 19, 8, 7, 35, 36, 46, 23, 25, 27, 39, 50, 20, 21, 32, 17, 21, 34, 24, 30, 21, 33, 22, 22, 5, 39, 19, 46, 23, 35, 39, 33, 20, 22, 19, 44, 44, 49, 24, 58, 41, 40, 25, 20, 25, 22, 22, 42, 45, 4, 26, 23, 29, 26, 52, 41, 1, 18, 36, 41, NA, 25, 29, 23, 26, 31, 35, 35, 34, 37, 31, 38, 23, 22, 17, 32, 1, 3, 25, 22, 29, 29, 22, 49, 5, 3, 8, 12, 40, 17, 32, 31, 39, 18, 20, 27, 41, 35, 37, 30, 25, 24, 25, 45, 42, 36, 30, 20, 55, 21, 16, 50, 61, 28, 27, 25, 34, 31, 19, 21, 29, 30, 50, 43, 20, 29, 50, 23, 32, 59, 30, 44, 20, 57, 21, 16, 24, 22, 30, 20, 20, 34, 29, 25, 1, 45, 24, 18, 32, 30, 28, 25, 29, 23, 35, 33, 18, 18, 30, 23, 36, 24, 46, 25, 55, 19, 29, 26, 24, 32, 32, 31, 20, 35, 22, 17, 42, 46, 19, 27, 19, 32, 62, 22, 32, 13, 42, 19, 48, 28, 31, 28, 25, 21, 28, 23, 65, 48, 44, 33, 37, 24, 21, 72, 23, 23, 30, 27, 55, 27, 47, 19, 7, 29, 28, 28, 54, 38, 34, 30, 29, 35, 28, 4, 7, 25, 17, 21, 25, 22, 18, 25, 31, 22, 18, 63, 22, 15, 17, 1, 21, 19, 36, 32, 20, 28, 14, 20, 36, 21, 2, 3, 32, 22, 26, 31, 23, 58, 19, 19, 60, 29, 28, 18, 26, 21, 11, 14, 41, 39, 20, 27, 21, 20, 29, 22, 60, 21, 26, 27, 18, 17, 23, 23, 24, 22, 21, 16, 39, 9, 28, 42, 27, 31, 28, 32, 20, 29, 23, 20, 20, 16, 22, 64, 21, 38, 36, 22, 27, 23, 29, 2, 6, 3, 8, 29, 1, 15, 7, 2, 16, 41, 17, 21, 59, 30, 40, 21, 20, 32, 23, 0.5833333333, 4, 26, 32, 22, 29, 28, 24, 21, 25, 22, 24, 19, 24, 39, 33, 25, 41, 28, 4, 2, 24, 28, 19, 29, 18, 25, 52, 21, 19, 43, 33, 32, 27, 17, 24, 24, 30, 46, 56, 27, 16, 2, 8, 33, 27, 21, 63, 30, 18, 36, 38, 33, 30, 37, 29, 21, 33, 10, 4, 7, 2, 8, 39, 23, 0.75, 3, 24, 58, 22, 22, 35, 22, 69, 64, 43, 50, 48, 45, 31, 18, 30, 45, 49, 39, 2, 41, 18, 33, 46, 36, 33, 26, 46, 54, 55, 59, 22, 17, 24, 30, 13, 18, 21, 61, 48, 30, 27, 47, 19, 17, 43, 13, 5, 8, 14, 10, 21, 18, 17, 20, 45, 45, 24, 24, 21, 43, 29, 17, 40, 16, 1, 4, 24, 21, 19, 24, 22, 35, 29, 22, 42, 28, 10, 30, 34, 35, 29, 27, 19, 55, 25, 26, 20, 22, 40, 17, 35, 39, 51, 40, 56, 20, 30, 22, 20, 40, 21, 18, 59, 5, 11, 9, 2, 43, 40, 42, 30, 35, 20, 28, 56, 37, 49, 56, 24, 57, 18, 18, 27, 24, 23, 25, 19, 31, 23, 6, 45, 39, 48, 57, 31, 32, 23, 33, 24, 22, 42, 62, 54, 43, 52, 64, 24, 61, 59, 19, 22, 30, 67, 63, 27, 2, 29, 20, 44, 25, 61, 74, 14, 24, 19, 14, 46, 0.4166666667, 30, 16, 18, 52, 39, 48, 40, 49, 25, 49, 17, 39, 34, 34, 16, 37, 38, 37, 33, 32, 20, 23, 22, 48, 41, 20, 26, 25, 23, 27, 27, 31, 8, 9, 27, 36, 18, 63, 26, 29, 39, 10, 9, 35, 28, 46, 61, 18, 31, 15, 31, 35, 28, 10, 36, 30, 23, 22, 40, 20, 14, 21, 22, 48, 36, 37, 35, 47, 32, 23, 31, 63, 60, 19, 12, 40, 23, 37, 66, 59, 37, 37, 2, 4, 29, 27, 0.8333333333, 4, 36, 33, 65, 26, 31, 54, 21, 55, 57, 31, 45, 51, 50, 27, 50, 18, 21, 47, 30, 21, 37, 21, 51, 23, 43, 28, 28, 21, 31, 21, 17, 54, 36, 32, 46, 26, 62, 27, 15, 36, 26, 45, 23, 24, 22, 27, 25, 29, 21, 34, 26, 20, 31, 37, 22, 24, 17, 26, 30, 36, 36, 41, 40, 48, 19, 35, 26, 41, 26, 21, 23, 37, 28, 33, 36, 46, 46, 24, 19, 24, 34, 19, 41, 42, 30, 36, 29, 40, 40, 16, 28, 33, 34, 36, 32, 26, 27, 53, 55, 33, 35, 24, 31, 35, 19, 51, 24, 30, 30, 33, 47, 22, 31, 39, 40, 26, 22, 31, 28, 20, 20, 20, 34, 23, 28, 41, 23, 26, 37, 32, 22, 45, 26, 30, 43, 59, 27, 40, 38, 33, 46, 24, 31, 28, 37, 36, 39, 29, 43, 30, 52, 34, 43, 22, 42, 38, 31, 28, 25, 31, 33, 34, 28, 25, 41, 34, 26, 22, 37, 21, 18, 30, 31, 20, 29, 35, 35, 23, 22, 31, 45, 30, 25, 40, 31, 27, 46, 33, 32, 44, 34, 37, 33, 28, 22, 39, 21, 25, 32, 25, 25, 28, 37, 29, 50, 25, 34, 40, 33, 31, 22, 29, 58, 33, 17, 38, 34, 25, 32, 45, 26, 26, 28, 27, 33, 19, 36, 28, 49, 30, 28, 30, 27, 23, 20, 43, 45, 21, 33, 39, 45, 44, 17, 30, 35, 49, 38, 35, 26, 18, 18, 32, 26, 33, 29, 39, 23, 19, 18, 35, 20, 29, 38, 20, 27, 37, 30, 34, 49, 39, 44, 27, 22, 39, 17, 35, 23, 20, 22, 27, 36, 48, 38, 24, 33, 41, 22, 44, 23, 23, 39, 39, 48, 23, 29, 34, 18, 43, 25, 27, 27, 33, 38, 22, 39, 37, 30, 29, 42, 33, 39, 30, 19, 23, 21, 29, 52, 24, 26, 46, 22, 33, 44, 22, 33, 27, 37, 27, 28, 30, 30, 21, 45, 29, 37, 32, 26, 36, 31, 32, 38, 38, 61, 36, 32, 45, 28, 29, 41, 40, 22, 26, 22, 28, 43, 45, 24, 24, 30, 29, 37, 35, 31, 22, 23, 41, 32, 47, 25, 54, 30, 39, 21, 21, 40, 24, 19, 17, 29, 17, 39, 40, 38, 54, 32, 21, 34, 23, 52, 43, 45, 24, 22, 30, 43, 28, 40, 26, 33, 45, 37, 29, 36, 25, 43, 40, 36, 31, 25, 19, 29, 26, 46, 43, 29, 22, 28, 24, 29, 29, 15, 40, 33, 23, 36, 38, 31, 34, 18, 32, 53, 23, 27, 41, 23, 31, 28, 19, 31, 19, 21, 20, 33, 30, 22, 59, 38, 27, 35, 29, 28, 55, 25, 24, 23, 38, 41, 17, 37, 29, 32, 21, 24, 32, 31, 38, 30, 40, 44, 25, 30, 18, 23, 22, 43, 37, 30, 44, 28, 26, 36, 34, 35, 18, 28, 20, 42, 48, 30, 36, 43, 31, 33, 49, 21, 43, 20, 41, 46, 34, 55, 40, 36, 22, 50, 18, 29, 41, 35, 25, 26, 27, 24, 20, 47, 38, 30, 40, 41, 29, 33, 31, 36, 20, 37, 29, 25, 31, 45, 26, 24, 34, 32, 36, 32, 40, 50, 27, 27, 21, 28, 40, 20, 39, 25, 32, 39, 30, 29, 20, 25, 30, 22, 59, 31, 28, 53, 25, 38, 23, 38, 47, 38, 36, 37, 29, 34, 30, 36, 17, 38, 21, 30, 40, 23, 21, 36, 44, 26, 32, 22, 34, 27, 28, 28, 26, 32, 51, 46, 53, 19, 20, 23, 24, 39, 32, 29, 22, 44, 42, 27, 26, 30, 28, 23, 36, 24, 37, 20, 39, 42, 26, 30, 33, 39, 48, 46, 35, 30, 42, 22, 21, 30, 30, 29, 44, 31, 27, 32, 27, 63, 31, 39, 32, 27, 36, 43, 17, 19, 32, 32, 29, 29, 45, 37, 27, 18, 45, 24, 28, 25, 21, 39, 34, 31, 49, 20, 17, 22, 37, 20, 48, 32, 19, 23, 26, 25, 30, 23, 27, 25, 35, 17, 33, 25, 34, 18, 24, 28, 33, 33, 36, 34, 22, 30, 23, 20, 17, 36, 23, 24, 21, 46, 22, 23, 20, 31, 22, 44, 29, 29, 50, 38, 21, 32, 20, 55, 34, 30, 31, 22, 32, 25, 19, 30, 36, 22, 30, 22, 29, 19, 37, 40, 35, 41, 30, 41, 31, 27, 36, 23, 26, 49, 31, 32, 24, 58, 17, 27, 28, 33, 24, 25, 47, 30, 24, 26, 22, 23, 23, 23, 31, 33, 42, 41, 28, 20, 55, 25, 24, 40, 31, 24, 25, 42, 40, 32, 20, 32, 25, 19, 29, 31, 24, 37, 37, 23, 23, 41, 33, 36, 28, 31, 30, 40, 29, 62, 22, 45, 40, 38, 27, 20, 39, 24, 35, 33, 26, 22, 41, 26, 21, 36, 38, 31, 47, 36, 30, 28, 20, 33, 29, 25, 19, 35, 34, 31, 28, 25, 48, 45, 24, 20, 24, 22, 40, 24, 42, 49, 19, 28, 32, 19, 27, 23, 35, 17, 46, 23, 30, 24, 25, 35, 39, 25, 44, 25, 27, 31, 29, 28, 30, 32, 39, 37, 42, 28, 17, 20, 35, 38, 36, 38, 33, 34, 21, 32, 19, 18, 25, 35, 37, 36, 48, 32, 23, 34, 31, 40, 37, 40, 37, 26, 26, 15, 27, 25, 31, 50, 28, 52, 21, 30, 30, 29, 18, 21, 32, 37, 27, 28, 32, 23, 39, 37, 39, 42, 26, 28, 35, 59, 34, 28, 53, 38, 36, 39, 31, 39, 37, 35, 40, 31, 49, 41, 45, 37, 19, 24, 40, 26, 41, 40, 32, 20, 26), class = structure(c(3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 3L, 3L, 3L, 3L, 1L, 3L, 1L, 1L, 1L, 1L, 3L, 3L, 3L, 3L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 1L, 1L, 3L, 2L, 2L, 1L, 3L, 3L, 3L, 1L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 3L, 3L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 3L, 2L, 2L, 3L, 3L, 3L, 1L, 1L, 3L, 3L, 1L, 2L, 3L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 3L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 3L, 2L, 3L, 2L, 1L, 1L, 3L, 1L, 3L, 1L, 1L, 1L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 2L, 3L, 3L, 3L, 1L, 3L, 2L, 1L, 2L, 3L, 1L, 3L, 2L, 1L, 3L, 3L, 1L, 1L, 2L, 2L, 3L, 3L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 3L, 3L, 1L, 3L, 3L, 3L, 3L, 1L, 3L, 2L, 2L, 3L, 1L, 2L, 2L, 3L, 3L, 3L, 3L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 2L, 2L, 3L, 3L, 1L, 3L, 3L, 3L, 2L, 1L, 1L, 3L, 3L, 1L, 1L, 1L, 3L, 2L, 1L, 1L, 2L, 1L, 1L, 3L, 1L, 1L, 1L, 1L, 3L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 1L, 1L, 1L, 1L, 3L, 1L, 3L, 2L, 2L, 3L, 3L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 3L, 1L, 1L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 1L, 3L, 2L, 3L, 3L, 3L, 3L, 1L, 3L, 3L, 3L, 1L, 1L, 1L, 3L, 1L, 1L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 3L, 3L, 3L, 1L, 1L, 3L, 1L, 1L, 2L, 3L, 2L, 3L, 3L, 3L, 2L, 2L, 3L, 3L, 2L, 3L, 3L, 3L, 3L, 3L, 2L, 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1L, 4L, 1L, 4L, 4L, 4L, 4L, 4L, 4L, 1L, 1L, 4L, 4L, 1L, 1L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 1L, 4L, 1L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 1L, 4L, 4L, 4L, 4L, 1L, 4L, 1L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 1L, 4L, 1L, 1L, 4L, 1L, 4L, 4L, 1L, 4L, 4L, 4L, 4L, 1L, 4L, 1L, 4L, 4L, 4L, 1L, 1L, 1L, 4L, 4L, 4L, 4L, 4L, 4L, 1L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 1L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 1L, 4L, 4L, 4L, 1L, 4L, 4L, 4L, 4L), .Label = c("Belfast", "Cherbourg", "Queenstown", "Southampton"), class = "factor"),     fare = c(7.11, 20.05, 20.05, 20.05, 7.13, 7.13, 24, 24, 18.1509,     7.1806, 7.05, 8.01, 9.0906, 9.07, 9.07, 13, 7.1709, 7.1806,     7.01, 7.01, 211.6009, 8.01, 151.16, 151.16, 151.16, 151.16,     8.06, 7.0407, 7.1701, 22.1006, 26.11, 31.0506, 31.0506, 31.0506,     7.1806, 7.1506, 31.0506, 31.0506, 31.0506, 7.1506, 31.0506,     7.1701, 11.1, 10.1, 77.1902, NA, 7.1711, 26, 26, 51.0907,     17.16, 17.16, 7.1506, 49.1001, 13, 7.01, 31.0709, 31.0709,     31.0709, 31.0709, 31.0709, 31.0709, 7.1511, 31.0709, 7.0406,     7.01, 247.1006, 247.1006, 14.0902, 7.0406, 69.06, 7.1701,     7.0407, 15.17, 15.17, 19.0502, 19.0502, 19.0502, 19.0502,     8.01, 7.0406, 11.1, 10.1, 7.1711, 13, 10.1, 8.1308, 14.0901,     14.0901, 78.17, 30, 7.1707, 8.01, 76.051, 12.1006, 7.1711,     25.1806, 247.1005, 247.1005, 26, 26, 75.041, 26, 8.01, 39,     39, 39, 39, 52.1101, 52.1101, 13, 30, 7.1506, 13, 9.07, 13,     63.0702, 247.1006, 56.0911, 221.1507, 7.1506, 26, 91.0107,     91.0107, 7.15, 26.11, 35.1, 31, 164.1704, 26.11, 26.11, 7.1711,     13, 7.15, 15.1, 15.1, 262.0706, 16.02, 13, 55, 13, 7.1406,     30.1, 7.1701, NA, 50.0911, 7, 7.05, 26.11, 39.12, NA, 13,     7.1511, 8.01, 13, 39, 51.0907, 39, 27.1405, 39, 26, 26, 7.0508,     7.1517, 76.051, 15.0411, 15.0411, 15.0411, 6.15, 134.1, 7.1707,     13, 13, 7.0407, 26.11, 13, 13, 8.1303, 8.1303, 8.1303, 8.1303,     31, 26.0509, 29, 29, 29, 8.1303, 8.1303, 21, NA, 7.15, 7.15,     27.0811, 8.01, 14.0902, 14.0902, 13, 512.0607, 512.0607,     7.1511, 7.1701, 5, 47.02, 47.02, 7.15, 26, 120, 120, 26,     120, 120, 7.05, 26, 27.1405, 78.17, 78.17, 8.01, 61.0306,     61.0306, 53.02, 53.02, 13.1, 26, 26, 7.1408, 262.0706, 86.1,     29.14, 55, 56.0911, NA, 8.01, 30, 30, 14.0901, 14.0901, 26,     NA, 27, 136.1507, 136.1507, 151.16, 52, 7.01, 8.01, 7.05,     7.1711, 10.1, 13, 10.1, 25.1109, 26.05, 26.05, 26.05, 7.1711,     83.0302, 83.0302, 83.0302, 7.1408, 7.15, 7.15, 7.1207, 7.15,     8.01, 10.1, 13, 21, 7.1711, 7.1711, 7.1711, 8.01, 25.141,     7.15, 11.1, 15.18, 15.18, 15.18, 7.05, 26.11, 8.0302, 16.02,     16.02, 71, 26.11, 71, 8.1303, 71.0508, 71.0508, NA, 7.0407,     10.1004, 8.01, 10.0305, 6.19, 7.15, 26.11, 14.08, 14.08,     14.08, 30.1, 151.16, 7.1711, 52, 52, 36.15, 24.03, 73.1,     8.01, 24.03, 8.01, 36.15, 13, 16.02, 16.02, 73.1, 20.1106,     20.1106, 20.1106, 20.1106, 7.1711, 27.1405, 27.1405, 17.08,     17.08, 9.1, 31.1, 7.1711, 7.05, 7.05, 9.1, 7.1707, 73.1,     57, 57, 7.1711, 8.1303, 7.1711, 81.1702, 81.1702, 81.1702,     23, 23, 7.15, 7.15, 8.01, 8.0902, 106.0806, 247.1005, 106.0806,     26, 12.0906, 7.15, 7.1711, 32.1, 32.1, 32.1, 7.15, 8.01,     56.1807, 39.12, 29.14, 7.11, 13.1702, 13.1702, 7.15, 13.18,     13.18, 83.0302, 7.1506, 13, 7.1506, 6.1906, 7.0406, 7.0407,     7.0407, 7.05, 12.0906, 13, 247.1006, 7.01, 78.0504, 31.1307,     15.02, 13, 7.05, 7.15, 221.1507, 26, 10.1, 7.01, 7.1511,     31.1308, 7.15, 110.1708, 7.15, 6.19, 26.0509, 7.1707, 7.15,     56.0911, 34.0706, 34.0706, 8.01, 34.0706, 34.0706, 34.0706,     27.15, 263, 263, 263, 263, 263, 263, 7.15, 13, 56.1807, 26.11,     133.13, 27.1405, 133.13, 49.1, 79.04, 79.04, NA, NA, 13,     53.02, 53.02, 26, 21, 21, 7.141, 14.1, 13, 26, 10.1, 38.1,     7.0407, 7.0406, 7.1711, 59.08, 59.08, 211.1, 79.04, 10.1,     11.1, 11.1, 13.1, 8.01, 13, 13, 7.1408, 13, 7.15, 89.0201,     89.0201, 34.1301, 20.1006, 20.1006, 7.17, 20.1006, 46.18,     46.18, 46.18, 46.18, 46.18, 46.18, 46.18, 46.18, 28.1, 153.0903,     30, 153.0903, 13, 63.0702, 63.0702, 0, 8.01, 8.0615, 8.01,     79.04, 9.1611, 7.1806, 7.1806, 7.1506, 8.17, 6.1904, 6.1904,     15.17, 15.17, 13, 14.1, 14.1, 9.1, 7.0407, 7.0406, 7.0407,     14.0202, 7.1701, 7.1701, 14.0202, 13, 55.081, 55.081, 7.1408,     7.11, 33, 76.1407, 76.1407, 33, 42.08, 10.1, 83.0906, 83.0906,     10.1, NA, NA, 26.05, 26.05, 6.1702, 26.05, 76.1407, 30, 83.0302,     93.1, 93.1, 42.1, 7.15, 6.1906, 56.0911, 6.15, 7.1806, 7.1806,     8.1903, 7.1711, 7.1506, 7.15, 65, 65, 65, 65, 16, 73.1, 73.1,     73.1, 51.1703, 13, 50, 57.1907, 57.1907, 12.0509, 12.0509,     23, 11.1, 13, 23, 13, 77.1902, 26, 26, 6.09, 22.1006, 52,     52, 26.11, 7.1806, 73.1, 7.15, 13, 8.01, 26, 26, 100, 100,     30.1311, 7.13, NA, 12.0506, 7.1709, 18.1509, 7.0407, 14.0902,     80, 10.1, 7.1711, 7.1806, 7.1806, 28.1403, NA, 7.1711, 14.0901,     14.0901, 27, 27, 7.1711, 7.1511, 7.01, 15, 31.1, 31.1, 10.1,     7.1701, 7.0101, 7.1701, 7.15, 13.151, 8.0203, 6.0911, 7.1511,     8.1301, 7.1506, 7.1701, 7.1506, 11.0208, 11.0208, NA, 7.1506,     11.0208, NA, 23.09, 23.09, 23.09, 23.09, 26, 7.1711, 7.1701,     7.1701, 7.0407, 26, 9.161, 9.161, 7.1806, 7.0206, 8.0808,     26, 26, 7.1711, 7.1511, 7.1701, 7.1005, 21, 13.0804, 13.0804,     7.0407, 12.07, 7.15, 12.07, 7.05, 211.1, 7.15, 7.15, 8.01,     7.1607, 0, 13.1, 7.15, 29.14, 51.1703, 51.1703, 14.0901,     14.0901, 7.15, 7.15, 7.1409, 52.1101, 52.1101, 22, 22, 8.1303,     22, 8.1303, 12.07, 26.11, 12.0308, 7.1701, 12.0308, NA, 7.1711,     7.0407, 211.0609, NA, 7.11, 10.1, 7.0406, 26, 26, 9.1109,     10.1402, 56.0911, 56.0911, 7.05, 7.15, 56.0911, 41.1107,     41.1107, 41.1107, 41.1107, 9.0908, 7.1506, 7.1506, 25.1807,     25.0904, 25.0904, 25.0904, 25.0904, 25.0904, 12, 7.1806,     33, 10.1, 15.1, 15.1, 106.0806, 24.03, 512.0607, 12.1706,     27.1405, 10.1, 9.1, 7.1506, 7.15, 26.11, 15.11, 15.11, 7.1806,     27.1405, 7.1707, 39.08, 39.08, 12.07, 56.0911, 9.1, 7.05,     16.02, 16.02, 7.1707, 77.1902, 30, 45.1, 26, 26, 7.05, 8.1303,     7.0101, 7.1701, 7.1107, 146.1005, 6.0809, 7.1711, 7.11, 10.1,     7.15, 211.0609, 7.021, 7.0206, 26, 7.1707, 86.1, 8.01, 7.1806,     15, 37, 37, 37, 7.0407, 7.15, 15.1107, 7.1409, 7.0407, 29.14,     7.1711, 7.1711, 7.1711, 53.02, 53.02, 7.1711, 13, 16, 49.1001,     75.041, 7.15, 51.1703, 7.15, 23.05, 23.05, 23.05, 13.1, 13,     7.1508, 15.1, 26.0509, 7.1707, 8, 7.15, 26, 7.15, 16.02,     16.02, 7.15, 8.01, 7.15, 8.01, 19.1, 19.1, 10.1, 8.01, 7.15,     13, 82.0305, 82.0305, 7.1506, 7.1711, 8.01, 26.11, 13, 90,     90, 90, 7.1711, 7.1711, 10.1, 7.1711, 7.1707, 57.15, 7.13,     30.1, 13, 42.08, 8.01, 12.0906, 12.0906, 24.03, 24.03, 14,     26, 8.01, 7.15, 7.1506, 7.0407, 8.01, 15.0411, 15.0411, 15.0411,     10.1, 7.1408, 7.15, 8.01, 15.1, 15.1, 15.1, 7.0406, 7.15,     9.1309, 7.1711, 7.0406, 7.0407, 15.141, 15.141, 15.141, 8.01,     7.1711, 7.0407, 30.0105, 30.0105, 7.0406, 29.14, 7.15, 26,     26, 26, 7.1711, 13, 113.0506, 113.0506, 113.0506, 26.0508,     36.15, 26, 7.1806, 8.01, 7.1506, 7.1701, 7.1701, 11.041,     11.041, 7.0206, 7.1806, 7.0407, 13.1, 7.16, 13.1703, 10.1,     7.15, 6.0409, 7.1607, 15.1, 15.1, 7.1408, 7.15, 7.15, 9.0406,     7.1707, 7.1506, 7.15, 7.1607, 108.18, 3.0305, 22.1006, 8.0801,     7.0603, 7.1701, 7.1701, 7.1506, 9.0406, 25.141, 8.1303, 8.1303,     8.1303, 9.0404, 61.1907, 61.1907, 7.1207, 13, 27.1405, 10.1,     13.1703, 10.1, 13.1702, 21.0106, 21.0106, 21.0106, 21.0106,     21.0106, 39.1309, 39.1309, 39.1309, 39.1309, 39.1309, 39.1309,     10.1, NA, 26, NA, 28.1, 8.1303, 14.1, 7.1711, 93.1, 13.1506,     13.1506, 13.1506, 7, 66.12, 66.12, 7.1506, 8.01, 7.1806,     7.1806, 108.18, 108.18, 10.1, 7.05, 15.01, 93.1, 7.1506,     13, 7.1711, 22.0702, 22.0702, 8.01, 8.0209, 7.1711, 7.1711,     7.1506, 7.1506, 30.1, 21, 26, 21, 8.01, 13, 7.1711, 8.1303,     8.1303, 13, 12.1409, 52, 83.0302, 15, 8.1403, 26, 26, 26,     7.1711, 7.0406, 8.0203, 7.0407, 7.05, 10.1, 7.1711, 21, 21,     NA, 13, 8.01, 39.12, 29.0206, 29.0206, 29.0206, 29.0206,     29.0206, 29.0206, 15, 18.15, 18.15, 18.15, 10.1, 135.1208,     39.1309, 7.0206, 7.1405, 14.1, 14.1, 211.0609, 14.1, 14.1,     247.1006, 50.0911, 10.1, 8.01, 26.11, 7.1506, 50, 20.0403,     20.0403, 20.0403, 27.1405, 79, 40.0206, 86.1, 8.01, 59.08,     59.08, 8.01, 26.11, 10.1, 8.01, 7.15, 24.03, 262.0706, 262.0706,     262.0706, 262.0706, 262.0706, 7.0407, 7.0406, 30.1, 7.1407,     7.111, 7.05, 69.11, 69.11, 69.11, 69.11, 69.11, 69.11, 69.11,     69.11, 69.11, 69.11, 69.11, 69.06, 9.0606, 7.13, 26, 7.1806,     21.1307, 21.1307, 21.1307, 16.14, 16.14, 16.14, 9.1, 8.01,     8.01, 7.1406, 57.15, 9.1, 13, 7.1711, 13, 4, 31, 26.11, NA,     26, 7.15, 7.0406, 15.02, 26, 7.0406, 7.1607, 8.01, 153.0903,     13, 26.0509, 55.18, 55.18, 8.01, 35.1, 36.15, 13, 7.0407,     8.01, 7.1711, 7.1806, 6.0911, 13.1, 27.18, 27.18, 27.18,     27.18, 27.18, 27.18, 7.1711, 12.07, 10.1, 8.01, 35.1, 26.11,     8.1303, 13, 30.1311, 60, 26, 60, 7.1408, 7.15, 82.0504, 82.0504,     13, 7.13, 8.01, 8.01, 134.1, 134.1, 134.1, 146.1005, 146.1005,     8.01, 30.1, 7.1711, 8.1309, 7.11, 8.01, 15, 26.11, 55.081,     55.081, 78.0504, 27.1405, 10.1, 80, NA, 7.1711, 9.1609, 7.1806,     221.1507, 221.1507, 8.1303, 10.0903, 10.0903, 8.01, 7.1806,     7.01, 32.0605, 7.1506, 9.0406, 7.1511, 13, 65, 25.1807, 8.1004,     6.0809, 8.1004, 79.13, 79.13, 79.13, 52, NA, 52, 7.1511,     110.1708, 110.1708, 110.1708, 8.01, 6.0809, 7.0406, 8.01,     80, 16.02, 16.02, 7.1806, 7.15, 7.1711, 8.01, 10.1, 8.01,     7.0407, NA, 7.1711, 13, 12.13, 10.1, 28.1009, 15.0411, 15.0411,     15.0411, 7.1711, 9.161, 9.1109, 21, 21, 27.1405, 14.1, 14.1,     14.1, 9.1, 9, 33.1, 18, 18, 18, 18, 9.1, 9.1, 24.03, 24.03,     24.03, 9.1, 7.0406, 13, 7.1701, 7.1701, 7.1711, 9, 34, 21,     512.0607, 8.01, 21, 21, 10.1, 7.11, 75.05, 75.05, NA, 15.15,     15.15, 7.0406, 13, 8.01, 26.11, 26, 26, 23, 23, 23, 7.1511,     27.15, 27.15, 27.15, 27.15, 10.1, 12.1706, 13, 77.0509, 77.0509,     135.1208, 164.1704, 164.1704, 164.1704, 7.15, 211.1, 211.1,     211.1, 6.0911, 6.0911, 7, 26, 26.11, 8.1403, 7.11, 61.0707,     13, 35, 8.01, 16.02, 61.0707, 134.1, 7.05, 8.1303, 7.05,     9.1, NA, 35.1, 13.1, 26.11, 14.0901, 14.0901, 135.1208, 7.0406,     7.0407, 22.0702, 13, 7.0406, 7.0406, 7.0406, 7.1706, 0, 0,     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), sibsp = c(0, 0, 1, 1,     0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,     1, 1, 1, 0, 0, 1, 0, 0, 4, 4, 4, 4, 4, 4, 4, 1, 0, 1, 0,     0, 0, 1, 0, 0, 1, 1, 2, 1, 1, 0, 0, 0, 0, 4, 4, 4, 4, 4,     1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 3, 2, 2, 2, 0, 0,     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,     1, 1, 0, 0, 0, 2, 2, 2, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0,     0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0,     0, 0, 0, 0, 0, 0, 0, NA, 0, 1, 1, 0, 0, NA, 0, 0, 0, 0, 0,     2, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0,     0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0,     1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0,     0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0,     1, 0, 1, 1, 1, NA, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,     0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,     0, 2, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0,     0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 2, 0, 0,     2, 2, 0, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0,     0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0,     1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0,     1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0,     0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 0,     2, 1, 1, 0, 3, 3, 3, 3, 1, 1, 0, 0, 0, 0, 2, 1, 1, 0, 1,     1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,     0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0,     1, 5, 5, 5, 5, 5, 5, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,     0, 0, 0, 2, 2, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 2,     1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, NA,     0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,     0, 1, 1, 1, 1, 0, 2, 2, 2, 0, 1, 0, 0, 0, 0, 1, 2, 2, 0,     1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1,     0, 0, NA, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 2,     1, 0, 0, 0, 0, 2, 2, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,     0, 1, 1, NA, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1,     0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,     0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 2,     3, 2, 0, 0, 1, 1, 0, 0, 0, 0, 0, NA, 0, 0, 0, 1, 1, 0, 0,     0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 3, 3, 3, 3, 0, 0,     0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0,     0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0,     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1,     0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0,     2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0,     0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 2, 1, 1, 0, 0, 0,     0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0,     1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1,     0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0,     0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1,     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 0, 4, 4,     4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1,     0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0,     0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1,     0, 0, 0, 0, 0, 0, 0, 0, 1, 3, 0, 0, 0, 0, 4, 4, 4, 4, 4,     0, 0, 1, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0,     0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0,     2, 2, 2, 1, 1, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 8, 8, 8, 8,     8, 1, 1, 0, 0, 0, 0, 0, 2, 2, 2, 1, 1, 0, 0, 0, 0, 0, 1,     0, 0, 0, 0, 0, 0, 0, NA, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,     1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 1, 1, 0,     0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0,     0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0,     0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,     0, 1, 1, 0, 1, 1, 1, NA, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1,     1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0,     0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 2, 3, 2, 1, 0, 0, 0, 1, 1,     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0,     0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0,     0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0,     0, 0, 0, 0, 0, 0, 0, 0, NA, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0,     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), parch = c(0,     2, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0,     0, 0, 2, 2, 2, 2, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 5,     0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2,     2, 2, 2, 5, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1,     1, 3, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0,     0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 3, 1, 1, 0, 0, 0, 0, 0,     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2,     1, 1, 0, 0, 0, 1, 0, 0, 0, 0, NA, 0, 0, 0, 0, 0, NA, 0, 0,     0, 0, 2, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 2, 0, 0, 0, 0,     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 1, 1, 0, 0, 0, 0, 0,     0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 2, 2, 0, 2,     2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,     0, 0, 0, 1, 2, 0, 0, 0, NA, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,     0, 0, 0, 2, 1, 1, 0, 1, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0,     0, 0, 0, 0, 0, 0, 0, 1, 1, 2, 0, 0, 0, 1, 1, 1, 2, 1, 0,     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 1, 1, 0, 0, 0, 2, 0, 1,     0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 2, 2, 2, 2, 0, 0, 0, 0, 0,     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 1, 1, 1, 0,     0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 2, 1, 1, 0, 0, 0, 0, 0, 0,     0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0,     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,     2, 2, 0, 2, 3, 3, 0, 2, 2, 2, 2, 4, 4, 0, 0, 0, 0, 0, 0,     0, 2, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,     0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,     2, 1, 0, 1, 2, 2, 2, 2, 2, 2, 6, 6, 0, 0, 1, 1, 0, 1, 1,     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0,     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0,     0, 0, NA, 2, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0,     0, 0, 0, 0, 2, 2, 2, 2, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1,     1, 1, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,     0, 0, 0, 0, 0, NA, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,     0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,     0, 0, 0, 0, 1, 1, NA, 0, 2, 0, 2, 2, 2, 2, 0, 0, 0, 0, 2,     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0,     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,     2, 1, 0, 1, 0, 0, 0, 1, 1, 2, 0, 0, 0, 0, NA, 0, 0, 0, 1,     1, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 0, 0, 0, 0, 1, 1, 1,     1, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,     0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,     2, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,     0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0,     0, 0, 0, 1, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,     2, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 2, 0, 0, 0, 0, 2,     2, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0,     0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1,     4, 1, 1, 1, 1, 1, 5, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 2,     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0,     0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0,     0, 1, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1,     1, 1, 1, 5, 0, 1, 1, 3, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0,     0, 0, 0, 0, 0, 0, 1, 2, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,     0, 0, 0, 2, 2, 2, 3, 3, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2,     2, 2, 2, 2, 9, 9, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 2, 0, 0,     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, NA, 0, 0, 0, 0, 1, 0, 0, 0,     0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2,     4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,     0, 0, 0, 2, 1, 1, 0, 0, 0, 0, 0, 0, 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0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,     0, 0)), class = "data.frame", row.names = c(NA, 2207L), terms = survived ~     gender + age + class + embarked + fare + sibsp + parch)): missing values in object
     model_titanic_rf
    #> Error in eval(expr, envir, enclos): object 'model_titanic_rf' not found
    
  explain_titanic_rf <- explain(model_titanic_rf,
                            data = titanic[,-9],
-                           y = titanic$survived)
-
+                           y = titanic$survived)
    #> Error in explain(model_titanic_rf, data = titanic[, -9], y = titanic$survived): object 'model_titanic_rf' not found
    
 selected_passangers <- select_sample(titanic, n = 100)
-cp_rf <- ceteris_paribus(explain_titanic_rf, selected_passangers)
-head(cp_rf)
    #> Top profiles    : 
-#>        gender age            class    embarked country   fare sibsp parch
-#> 1960     male  36 victualling crew Southampton England 0.0000     0     0
-#> 1960.1 female  36 victualling crew Southampton England 0.0000     0     0
-#> 883      male  21              3rd Southampton  Sweden 7.1701     0     0
-#> 883.1  female  21              3rd Southampton  Sweden 7.1701     0     0
-#> 1594     male  44 victualling crew Southampton England 0.0000     0     0
-#> 1594.1 female  44 victualling crew Southampton England 0.0000     0     0
-#>        _yhat_ _vname_ _ids_      _label_
-#> 1960    0.000  gender  1960 randomForest
-#> 1960.1  0.892  gender  1960 randomForest
-#> 883     0.004  gender   883 randomForest
-#> 883.1   0.456  gender   883 randomForest
-#> 1594    0.000  gender  1594 randomForest
-#> 1594.1  0.954  gender  1594 randomForest
-#> 
-#> 
-#> Top observations:
-#>      gender age            class    embarked country   fare sibsp parch _yhat_
-#> 1960 female  36 victualling crew Southampton England 0.0000     0     0  0.892
-#> 883    male  21              3rd Southampton  Sweden 7.1701     0     0  0.004
-#> 1594   male  44 victualling crew Southampton England 0.0000     0     0  0.000
-#> 1131   male  37              3rd Southampton Croatia 8.1303     0     0  0.002
-#> 1445   male  20 victualling crew Southampton England 0.0000     0     0  0.002
-#> 1432   male  33 victualling crew     Belfast England 0.0000     0     0  0.000
-#>           _label_ _ids_
-#> 1960 randomForest     1
-#> 883  randomForest     2
-#> 1594 randomForest     3
-#> 1131 randomForest     4
-#> 1445 randomForest     5
-#> 1432 randomForest     6
    
-pdp_rf_p <- aggregate_profiles(cp_rf, variables = "age", type = "partial")
-pdp_rf_p$`_label_` <- "RF_partial"
-pdp_rf_c <- aggregate_profiles(cp_rf, variables = "age", type = "conditional")
-pdp_rf_c$`_label_` <- "RF_conditional"
-pdp_rf_a <- aggregate_profiles(cp_rf, variables = "age", type = "accumulated")
-pdp_rf_a$`_label_` <- "RF_accumulated"
-plot(pdp_rf_p, pdp_rf_c, pdp_rf_a, color = "_label_")
    
+cp_rf <- ceteris_paribus(explain_titanic_rf, selected_passangers)
    #> Error in ceteris_paribus(explain_titanic_rf, selected_passangers): object 'explain_titanic_rf' not found
    head(cp_rf)
    #> Error in head(cp_rf): object 'cp_rf' not found
    
+pdp_rf_p <- aggregate_profiles(cp_rf, variables = "age", type = "partial")
    #> Error in aggregate_profiles(cp_rf, variables = "age", type = "partial"): object 'cp_rf' not found
    pdp_rf_p$`_label_` <- "RF_partial"
    #> Error in pdp_rf_p$`_label_` <- "RF_partial": object 'pdp_rf_p' not found
    pdp_rf_c <- aggregate_profiles(cp_rf, variables = "age", type = "conditional")
    #> Error in aggregate_profiles(cp_rf, variables = "age", type = "conditional"): object 'cp_rf' not found
    pdp_rf_c$`_label_` <- "RF_conditional"
    #> Error in pdp_rf_c$`_label_` <- "RF_conditional": object 'pdp_rf_c' not found
    pdp_rf_a <- aggregate_profiles(cp_rf, variables = "age", type = "accumulated")
    #> Error in aggregate_profiles(cp_rf, variables = "age", type = "accumulated"): object 'cp_rf' not found
    pdp_rf_a$`_label_` <- "RF_accumulated"
    #> Error in pdp_rf_a$`_label_` <- "RF_accumulated": object 'pdp_rf_a' not found
    plot(pdp_rf_p, pdp_rf_c, pdp_rf_a, color = "_label_")
    #> Error in plot(pdp_rf_p, pdp_rf_c, pdp_rf_a, color = "_label_"): object 'pdp_rf_p' not found
    
 pdp_rf <- aggregate_profiles(cp_rf, variables = "age",
-                             groups = "gender")
-head(pdp_rf)
    #>   _vname_             _label_        _x_ _groups_    _yhat_ _ids_
-#> 1     age randomForest_female  0.1666667   female 0.7610000     0
-#> 2     age randomForest_female  2.0000000   female 0.7462727     0
-#> 3     age randomForest_female  4.0000000   female 0.7898182     0
-#> 4     age randomForest_female  7.0000000   female 0.7720000     0
-#> 5     age randomForest_female  9.0000000   female 0.7770000     0
-#> 6     age randomForest_female 13.0000000   female 0.7958182     0
    plot(cp_rf, variables = "age") +
+                             groups = "gender")
    #> Error in aggregate_profiles(cp_rf, variables = "age", groups = "gender"): object 'cp_rf' not found
    head(pdp_rf)
    #> Error in head(pdp_rf): object 'pdp_rf' not found
    plot(cp_rf, variables = "age") +
   show_observations(cp_rf, variables = "age") +
   show_rugs(cp_rf, variables = "age", color = "red") +
-  show_aggreagated_profiles(pdp_rf, size = 3, color = "_label_")
    + show_aggregated_profiles(pdp_rf, size = 3, color = "_label_")
#> Error in plot(cp_rf, variables = "age"): object 'cp_rf' not found

plot(clust_rf, color = "_label_") + - show_aggreagated_profiles(pdp_rf, color = "black", size = 3)
+ show_aggregated_profiles(pdp_rf, color = "black", size = 3)
plot(cp_rf, color = "grey", variables = "age") + - show_aggreagated_profiles(clust_rf, color = "_label_", size = 2)
+ show_aggregated_profiles(clust_rf, color = "_label_", size = 2)
clust_rf <- cluster_profiles(cp_rf, k = 3, center = TRUE, variables = "age") head(clust_rf)
#> _vname_ _label_ _x_ _cluster_ _yhat_ _ids_ #> 1 age Random Forest v7_1 0.1666667 1 0.7170289 0 diff --git a/docs/reference/feature_importance-5.png b/docs/reference/feature_importance-5.png index e56d8466..5ff9b608 100644 Binary files a/docs/reference/feature_importance-5.png and b/docs/reference/feature_importance-5.png differ diff --git a/docs/reference/feature_importance.html b/docs/reference/feature_importance.html index 7ccde646..decb2a14 100644 --- a/docs/reference/feature_importance.html +++ b/docs/reference/feature_importance.html @@ -219,8 +219,7 @@

Examp #> 4 salary 0.4149351 lm #> 5 gender 0.4151134 lm #> 6 evaluation 0.4291616 lm

plot(vd_glm)
-library("xgboost") -model_martix_train <- model.matrix(status == "fired" ~ . -1, HR) +library("xgboost")
#> Warning: package ‘xgboost’ was built under R version 3.5.2
model_martix_train <- model.matrix(status == "fired" ~ . -1, HR) data_train <- xgb.DMatrix(model_martix_train, label = HR$status == "fired") param <- list(max_depth = 2, eta = 1, silent = 1, nthread = 2, objective = "binary:logistic", eval_metric = "auc") @@ -231,10 +230,10 @@

Examp head(vd_xgb)

#> variable dropout_loss label #> 1 _full_model_ 0.3142729 xgboost #> 2 gendermale 0.3142729 xgboost -#> 3 evaluation 0.3372445 xgboost -#> 4 genderfemale 0.3909165 xgboost -#> 5 age 0.4038176 xgboost -#> 6 salary 0.4158528 xgboost
plot(vd_xgb, vd_glm)
+#> 3 evaluation 0.3382139 xgboost +#> 4 genderfemale 0.3916174 xgboost +#> 5 age 0.3979185 xgboost +#> 6 salary 0.4117663 xgboost
plot(vd_xgb, vd_glm)
+#> no 1321 97 0.06840621 +#> yes 305 376 0.44787078
explain_titanic_rf <- explain(model_titanic_rf, data = titanic[,-9], y = titanic$survived, diff --git a/docs/reference/plot.aggregated_profiles_explainer-1.png b/docs/reference/plot.aggregated_profiles_explainer-1.png index a29f9a0d..6f82bbc9 100644 Binary files a/docs/reference/plot.aggregated_profiles_explainer-1.png and b/docs/reference/plot.aggregated_profiles_explainer-1.png differ diff --git a/docs/reference/plot.aggregated_profiles_explainer-2.png b/docs/reference/plot.aggregated_profiles_explainer-2.png index 50f0cb71..140d5403 100644 Binary files a/docs/reference/plot.aggregated_profiles_explainer-2.png and b/docs/reference/plot.aggregated_profiles_explainer-2.png differ diff --git a/docs/reference/plot.aggregated_profiles_explainer-3.png b/docs/reference/plot.aggregated_profiles_explainer-3.png index 02194bb4..632c1f0a 100644 Binary files a/docs/reference/plot.aggregated_profiles_explainer-3.png and b/docs/reference/plot.aggregated_profiles_explainer-3.png differ diff --git a/docs/reference/plot.aggregated_profiles_explainer-4.png b/docs/reference/plot.aggregated_profiles_explainer-4.png index 53703459..b455f896 100644 Binary files a/docs/reference/plot.aggregated_profiles_explainer-4.png and b/docs/reference/plot.aggregated_profiles_explainer-4.png differ diff --git a/docs/reference/plot.aggregated_profiles_explainer.html b/docs/reference/plot.aggregated_profiles_explainer.html index 0b68f89e..3a7d863d 100644 --- a/docs/reference/plot.aggregated_profiles_explainer.html +++ b/docs/reference/plot.aggregated_profiles_explainer.html @@ -104,7 +104,7 @@

Adds a Layer with Aggregated Profiles

-

Function 'show_aggreagated_profiles' adds a layer to a plot created with 'plot.ceteris_paribus_explainer'.

+

Function 'show_aggregated_profiles' adds a layer to a plot created with 'plot.ceteris_paribus_explainer'.

# S3 method for aggregated_profiles_explainer
@@ -172,12 +172,12 @@ 
Examp
 pdp_rf_a<- accumulated_dependency(explain_titanic_glm, N = 50)
 pdp_rf_a$`_label_` <- "RF_accumulated"
 head(pdp_rf_p)
#>   _vname_    _label_       _x_    _yhat_ _ids_
-#> 1    fare RF_partial 0.0000000 0.2874121     0
-#> 2   sibsp RF_partial 0.0000000 0.3200546     0
-#> 3   parch RF_partial 0.0000000 0.3200546     0
-#> 4     age RF_partial 0.1666667 0.3693770     0
-#> 5   parch RF_partial 0.2800000 0.3200546     0
-#> 6   sibsp RF_partial 1.0000000 0.3200546     0
plot(pdp_rf_p, pdp_rf_l, pdp_rf_a, color = "_label_")

+#> 1    fare RF_partial 0.0000000 0.3138426     0
+#> 2   sibsp RF_partial 0.0000000 0.3297662     0
+#> 3   parch RF_partial 0.0000000 0.3297662     0
+#> 4     age RF_partial 0.1666667 0.3715088     0
+#> 5   parch RF_partial 0.2800000 0.3297662     0
+#> 6   sibsp RF_partial 1.0000000 0.3297662     0
plot(pdp_rf_p, pdp_rf_l, pdp_rf_a, color = "_label_")

  
 library("randomForest")
  titanic <- na.omit(titanic)
  model_titanic_rf <- randomForest(survived == "yes" ~ gender + age + class + embarked +
@@ -188,7 +188,7 @@ 
Examp
 #>                      Number of trees: 500
 #> No. of variables tried at each split: 2
 #> 
-#>           Mean of squared residuals: 0.1428164
+#>           Mean of squared residuals: 0.1428201
 #>                     % Var explained: 34.84

  explain_titanic_rf <- explain(model_titanic_rf,
                            data = titanic[,-9],
@@ -206,12 +206,12 @@ 
Examp
 #> 1594     male  44 victualling crew Southampton England 0.0000     0     0
 #> 1594.1 female  44 victualling crew Southampton England 0.0000     0     0
 #>           _yhat_ _vname_ _ids_          _label_
-#> 1960   0.1583068  gender  1960 Random Forest v7
-#> 1960.1 0.7717861  gender  1960 Random Forest v7
-#> 883    0.1193948  gender   883 Random Forest v7
-#> 883.1  0.4775326  gender   883 Random Forest v7
-#> 1594   0.1414053  gender  1594 Random Forest v7
-#> 1594.1 0.8851591  gender  1594 Random Forest v7
+#> 1960   0.1588032  gender  1960 Random Forest v7
+#> 1960.1 0.7758566  gender  1960 Random Forest v7
+#> 883    0.1140935  gender   883 Random Forest v7
+#> 883.1  0.4709515  gender   883 Random Forest v7
+#> 1594   0.1430898  gender  1594 Random Forest v7
+#> 1594.1 0.8663689  gender  1594 Random Forest v7
 #> 
 #> 
 #> Top observations:
@@ -223,12 +223,12 @@ 
Examp
 #> 1445   male  20 victualling crew Southampton England 0.0000     0     0
 #> 1432   male  33 victualling crew     Belfast England 0.0000     0     0
 #>          _yhat_          _label_ _ids_
-#> 1960 0.77178609 Random Forest v7     1
-#> 883  0.11939482 Random Forest v7     2
-#> 1594 0.14140527 Random Forest v7     3
-#> 1131 0.07144135 Random Forest v7     4
-#> 1445 0.22359315 Random Forest v7     5
-#> 1432 0.17579153 Random Forest v7     6

+#> 1960 0.77585659 Random Forest v7     1
+#> 883  0.11409353 Random Forest v7     2
+#> 1594 0.14308976 Random Forest v7     3
+#> 1131 0.07799722 Random Forest v7     4
+#> 1445 0.21975678 Random Forest v7     5
+#> 1432 0.16557374 Random Forest v7     6

 pdp_rf_p <- aggregate_profiles(cp_rf, variables = "age", type = "partial")
 pdp_rf_p$`_label_` <- "RF_partial"
 pdp_rf_c <- aggregate_profiles(cp_rf, variables = "age", type = "conditional")
@@ -236,16 +236,16 @@ 
Examp
 pdp_rf_a <- aggregate_profiles(cp_rf, variables = "age", type = "accumulated")
 pdp_rf_a$`_label_` <- "RF_accumulated"
 head(pdp_rf_p)
#>   _vname_    _label_        _x_    _yhat_ _ids_
-#> 1     age RF_partial  0.1666667 0.5421567     0
-#> 2     age RF_partial  2.0000000 0.5772933     0
-#> 3     age RF_partial  4.0000000 0.5815934     0
-#> 4     age RF_partial  7.0000000 0.5527687     0
-#> 5     age RF_partial  9.0000000 0.5497517     0
-#> 6     age RF_partial 13.0000000 0.5047613     0
plot(pdp_rf_p)
plot(pdp_rf_p, pdp_rf_c, pdp_rf_a)

+#> 1     age RF_partial  0.1666667 0.5304977     0
+#> 2     age RF_partial  2.0000000 0.5554616     0
+#> 3     age RF_partial  4.0000000 0.5539585     0
+#> 4     age RF_partial  7.0000000 0.5210260     0
+#> 5     age RF_partial  9.0000000 0.5207317     0
+#> 6     age RF_partial 13.0000000 0.4811878     0
plot(pdp_rf_p)
plot(pdp_rf_p, pdp_rf_c, pdp_rf_a)

 plot(cp_rf, variables = "age") +
   show_observations(cp_rf, variables = "age") +
   show_rugs(cp_rf, variables = "age", color = "red") +
-  show_aggreagated_profiles(pdp_rf_p, size = 2)

+  show_aggregated_profiles(pdp_rf_p, size = 2)
#> variable dropout_loss label #> 1 _full_model_ 0.3142729 xgboost #> 2 gendermale 0.3142729 xgboost -#> 3 evaluation 0.3401847 xgboost -#> 4 genderfemale 0.3937621 xgboost -#> 5 age 0.4006677 xgboost -#> 6 salary 0.4146221 xgboost
+#> 3 evaluation 0.3375758 xgboost +#> 4 genderfemale 0.3876988 xgboost +#> 5 age 0.3971998 xgboost +#> 6 salary 0.4113673 xgboost
plot(vd_glm, vd_xgb, bar_width = 5)
diff --git a/docs/reference/plotD3.html b/docs/reference/plotD3.html index 836a05a0..4a0c079a 100644 --- a/docs/reference/plotD3.html +++ b/docs/reference/plotD3.html @@ -157,20 +157,23 @@

Value

Examples

library("DALEX")
 library("ingredients")
-library("caret")
-
+library("caret")
#> Loading required package: lattice
#> Loading required package: ggplot2
#> Warning: package ‘ggplot2’ was built under R version 3.5.2
#> RStudio Community is a great place to get help:
+#> https://community.rstudio.com/c/tidyverse.
#> 
+#> Attaching package: ‘ggplot2’
#> The following object is masked from ‘package:randomForest’:
+#> 
+#>     margin

 rf_model <- train(m2.price~., data = apartments, method="rf", ntree = 100)
 explainer_rf <- explain(rf_model, data = apartments_test[,2:6],
                         y = apartments_test$m2.price, label="rf")
 fi_rf <- feature_importance(explainer_rf, loss_function = loss_root_mean_square)
 
 head(fi_rf)
#>            variable dropout_loss label
-#> 1      _full_model_     157.8401    rf
-#> 2          no.rooms     165.1408    rf
-#> 3 construction.year     369.3096    rf
-#> 4             floor     418.9614    rf
-#> 5           surface     602.1783    rf
-#> 6          district     961.8930    rf
plotD3(fi_rf)
+#> 1      _full_model_     159.6260    rf
+#> 2          no.rooms     169.6482    rf
+#> 3 construction.year     368.1726    rf
+#> 4             floor     417.9736    rf
+#> 5           surface     592.5975    rf
+#> 6          district     960.6579    rf
plotD3(fi_rf)
 
 svm_model <- train(m2.price~., data = apartments, method="svmLinear")
 explainer_svm <- explain(svm_model, data = apartments_test[,2:6],
@@ -179,11 +182,11 @@ 
Examp
 
 head(fi_svm)
#>            variable dropout_loss label
 #> 1      _full_model_     301.5889   svm
-#> 2 construction.year     301.5907   svm
-#> 3          no.rooms     317.8417   svm
-#> 4             floor     502.6867   svm
-#> 5           surface     615.7759   svm
-#> 6          district    1008.2428   svm
plotD3(fi_rf, fi_svm)
+#> 2 construction.year     301.6034   svm
+#> 3          no.rooms     316.6314   svm
+#> 4             floor     498.8596   svm
+#> 5           surface     610.8330   svm
+#> 6          district     995.0613   svm
plotD3(fi_rf, fi_svm)
 
 plotD3(fi_rf, fi_svm, split = "feature")
 
diff --git a/docs/reference/show_aggregated_profiles-1.png b/docs/reference/show_aggregated_profiles-1.png
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diff --git a/docs/reference/show_aggregated_profiles.html b/docs/reference/show_aggregated_profiles.html
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+Adds a Layer with Aggregated Profiles — show_aggregated_profiles • ingredients
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Adds a Layer with Aggregated Profiles

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Function 'show_aggregated_profiles' adds a layer to a plot created with 'plot.ceteris_paribus_explainer'.

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show_aggregated_profiles(x, ..., size = 0.5, alpha = 1,
+  color = "#371ea3", variables = NULL)

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a ceteris paribus explainer produced with function `ceteris_paribus()`
...
other explainers that shall be plotted together
size
a numeric. Size of lines to be plotted
alpha
a numeric between 0 and 1. Opacity of lines
color
a character. Either name of a color or name of a variable that should be used for coloring
variables
if not NULL then only `variables` will be presented

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+    
library("DALEX")
+# Toy examples, because CRAN angels ask for them
+titanic <- na.omit(titanic)
+selected_passangers <- select_sample(titanic, n = 100)
+
+model_titanic_glm <- glm(survived == "yes" ~ gender + age + fare,
+                       data = titanic, family = "binomial")
+
+explain_titanic_glm <- explain(model_titanic_glm,
+                           data = titanic[,-9],
+                           y = titanic$survived == "yes")
+
+cp_rf <- ceteris_paribus(explain_titanic_glm, selected_passangers)
+pdp_rf <- aggregate_profiles(cp_rf, variables = "age")
+plot(cp_rf, variables = "age") +
+  show_observations(cp_rf, variables = "age") +
+  show_aggregated_profiles(pdp_rf, size = 3)

+ 
library("randomForest")
+ model_titanic_rf <- randomForest(survived ~ gender + age + class + embarked +
+                                    fare + sibsp + parch,  data = titanic)
+ model_titanic_rf
#> 
+#> Call:
+#>  randomForest(formula = survived ~ gender + age + class + embarked +      fare + sibsp + parch, data = titanic) 
+#>                Type of random forest: classification
+#>                      Number of trees: 500
+#> No. of variables tried at each split: 2
+#> 
+#>         OOB estimate of  error rate: 19.2%
+#> Confusion matrix:
+#>       no yes class.error
+#> no  1317 101  0.07122708
+#> yes  302 379  0.44346549

+ explain_titanic_rf <- explain(model_titanic_rf,
+                           data = titanic[,-9],
+                           y = titanic$survived)
+
+cp_rf <- ceteris_paribus(explain_titanic_rf, selected_passangers)
+cp_rf
#> Top profiles    : 
+#>        gender age            class    embarked country   fare sibsp parch
+#> 1960     male  36 victualling crew Southampton England 0.0000     0     0
+#> 1960.1 female  36 victualling crew Southampton England 0.0000     0     0
+#> 883      male  21              3rd Southampton  Sweden 7.1701     0     0
+#> 883.1  female  21              3rd Southampton  Sweden 7.1701     0     0
+#> 1594     male  44 victualling crew Southampton England 0.0000     0     0
+#> 1594.1 female  44 victualling crew Southampton England 0.0000     0     0
+#>        _yhat_ _vname_ _ids_      _label_
+#> 1960    0.002  gender  1960 randomForest
+#> 1960.1  0.862  gender  1960 randomForest
+#> 883     0.002  gender   883 randomForest
+#> 883.1   0.538  gender   883 randomForest
+#> 1594    0.002  gender  1594 randomForest
+#> 1594.1  0.950  gender  1594 randomForest
+#> 
+#> 
+#> Top observations:
+#>      gender age            class    embarked country   fare sibsp parch _yhat_
+#> 1960 female  36 victualling crew Southampton England 0.0000     0     0  0.862
+#> 883    male  21              3rd Southampton  Sweden 7.1701     0     0  0.002
+#> 1594   male  44 victualling crew Southampton England 0.0000     0     0  0.002
+#> 1131   male  37              3rd Southampton Croatia 8.1303     0     0  0.004
+#> 1445   male  20 victualling crew Southampton England 0.0000     0     0  0.002
+#> 1432   male  33 victualling crew     Belfast England 0.0000     0     0  0.002
+#>           _label_ _ids_
+#> 1960 randomForest     1
+#> 883  randomForest     2
+#> 1594 randomForest     3
+#> 1131 randomForest     4
+#> 1445 randomForest     5
+#> 1432 randomForest     6

+pdp_rf <- aggregate_profiles(cp_rf, variables = "age")
+head(pdp_rf)
#>   _vname_      _label_        _x_  _yhat_ _ids_
+#> 1     age randomForest  0.1666667 0.51946     0
+#> 2     age randomForest  2.0000000 0.56250     0
+#> 3     age randomForest  4.0000000 0.56690     0
+#> 4     age randomForest  7.0000000 0.51762     0
+#> 5     age randomForest  9.0000000 0.51634     0
+#> 6     age randomForest 13.0000000 0.44654     0

+plot(cp_rf, variables = "age") +
+  show_observations(cp_rf, variables = "age") +
+  show_rugs(cp_rf, variables = "age", color = "red") +
+  show_aggregated_profiles(pdp_rf, size = 3)

+plot(pdp_rf, variables = "age", color = "grey")

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diff --git a/docs/reference/show_residuals.html b/docs/reference/show_residuals.html
index 23b00bed..ffb49ff1 100644
--- a/docs/reference/show_residuals.html
+++ b/docs/reference/show_residuals.html
@@ -185,7 +185,7 @@ 
Examp
                             variable_splits = list(age = seq(0,70, length.out = 1000)))
 
 plot(cp_johny, variables = "age", size = 1.5, color = "#8bdcbe") +
-  show_profiles(cp_neighbours, variables = "age", color = "#ceced9") +
+  show_profiles(cp_neighbours, variables = "age", color = "#ceced9") +
  show_observations(cp_johny, variables = "age", size = 5, color = "#371ea3") +
  show_residuals(cp_neighbours, variables = "age")
diff --git a/docs/reference/show_rugs.html b/docs/reference/show_rugs.html index 12b93194..6b1b2e49 100644 --- a/docs/reference/show_rugs.html +++ b/docs/reference/show_rugs.html @@ -170,7 +170,7 @@

Examp pdp_rf <- aggregate_profiles(cp_rf, variables = "age") plot(cp_rf, variables = "age") + show_observations(cp_rf, variables = "age") + - show_aggreagated_profiles(pdp_rf, size = 3)
+ show_aggregated_profiles(pdp_rf, size = 3)
library("randomForest") model_titanic_rf <- randomForest(survived ~ gender + age + class + embarked + fare + sibsp + parch, data = titanic) @@ -234,7 +234,7 @@

Examp plot(cp_rf, variables = "age") + show_observations(cp_rf, variables = "age") + show_rugs(cp_rf, variables = "age", color = "red") + - show_aggreagated_profiles(pdp_rf, size = 3)

+ show_aggregated_profiles(pdp_rf, size = 3)
plot(pdp_rf, variables = "age", color = "grey")
diff --git a/man/aggregate_profiles.Rd b/man/aggregate_profiles.Rd index 47537447..5b53a19b 100644 --- a/man/aggregate_profiles.Rd +++ b/man/aggregate_profiles.Rd @@ -59,7 +59,7 @@ head(pdp_rf) plot(cp_rf, variables = "age") + show_observations(cp_rf, variables = "age") + show_rugs(cp_rf, variables = "age", color = "red") + - show_aggreagated_profiles(pdp_rf, size = 3, color = "_label_") + show_aggregated_profiles(pdp_rf, size = 3, color = "_label_") } } \references{ diff --git a/man/cluster_profiles.Rd b/man/cluster_profiles.Rd index 1d238ece..270d7d95 100644 --- a/man/cluster_profiles.Rd +++ b/man/cluster_profiles.Rd @@ -66,10 +66,10 @@ clust_rf <- cluster_profiles(cp_rf, k = 3, variables = "age") head(clust_rf) plot(clust_rf, color = "_label_") + - show_aggreagated_profiles(pdp_rf, color = "black", size = 3) + show_aggregated_profiles(pdp_rf, color = "black", size = 3) plot(cp_rf, color = "grey", variables = "age") + - show_aggreagated_profiles(clust_rf, color = "_label_", size = 2) + show_aggregated_profiles(clust_rf, color = "_label_", size = 2) clust_rf <- cluster_profiles(cp_rf, k = 3, center = TRUE, variables = "age") head(clust_rf) diff --git a/man/plot.aggregated_profiles_explainer.Rd b/man/plot.aggregated_profiles_explainer.Rd index a611e090..cc30a202 100644 --- a/man/plot.aggregated_profiles_explainer.Rd +++ b/man/plot.aggregated_profiles_explainer.Rd @@ -27,7 +27,7 @@ a ggplot2 layer } \description{ -Function 'show_aggreagated_profiles' adds a layer to a plot created with 'plot.ceteris_paribus_explainer'. +Function 'show_aggregated_profiles' adds a layer to a plot created with 'plot.ceteris_paribus_explainer'. } \examples{ library("DALEX") @@ -78,7 +78,7 @@ plot(pdp_rf_p, pdp_rf_c, pdp_rf_a) plot(cp_rf, variables = "age") + show_observations(cp_rf, variables = "age") + show_rugs(cp_rf, variables = "age", color = "red") + - show_aggreagated_profiles(pdp_rf_p, size = 2) + show_aggregated_profiles(pdp_rf_p, size = 2) } } diff --git a/man/show_aggreagated_profiles.Rd b/man/show_aggregated_profiles.Rd similarity index 86% rename from man/show_aggreagated_profiles.Rd rename to man/show_aggregated_profiles.Rd index 324d6add..0646b4b9 100644 --- a/man/show_aggreagated_profiles.Rd +++ b/man/show_aggregated_profiles.Rd @@ -1,10 +1,10 @@ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/show_aggregated_profiles.R -\name{show_aggreagated_profiles} -\alias{show_aggreagated_profiles} +\name{show_aggregated_profiles} +\alias{show_aggregated_profiles} \title{Adds a Layer with Aggregated Profiles} \usage{ -show_aggreagated_profiles(x, ..., size = 0.5, alpha = 1, +show_aggregated_profiles(x, ..., size = 0.5, alpha = 1, color = "#371ea3", variables = NULL) } \arguments{ @@ -24,7 +24,7 @@ show_aggreagated_profiles(x, ..., size = 0.5, alpha = 1, a ggplot2 layer } \description{ -Function 'show_aggreagated_profiles' adds a layer to a plot created with 'plot.ceteris_paribus_explainer'. +Function 'show_aggregated_profiles' adds a layer to a plot created with 'plot.ceteris_paribus_explainer'. } \examples{ library("DALEX") @@ -43,7 +43,7 @@ cp_rf <- ceteris_paribus(explain_titanic_glm, selected_passangers) pdp_rf <- aggregate_profiles(cp_rf, variables = "age") plot(cp_rf, variables = "age") + show_observations(cp_rf, variables = "age") + - show_aggreagated_profiles(pdp_rf, size = 3) + show_aggregated_profiles(pdp_rf, size = 3) \donttest{ library("randomForest") @@ -64,7 +64,7 @@ head(pdp_rf) plot(cp_rf, variables = "age") + show_observations(cp_rf, variables = "age") + show_rugs(cp_rf, variables = "age", color = "red") + - show_aggreagated_profiles(pdp_rf, size = 3) + show_aggregated_profiles(pdp_rf, size = 3) plot(pdp_rf, variables = "age", color = "grey") diff --git a/man/show_rugs.Rd b/man/show_rugs.Rd index aee68dce..646b8e79 100644 --- a/man/show_rugs.Rd +++ b/man/show_rugs.Rd @@ -48,7 +48,7 @@ cp_rf <- ceteris_paribus(explain_titanic_glm, selected_passangers) pdp_rf <- aggregate_profiles(cp_rf, variables = "age") plot(cp_rf, variables = "age") + show_observations(cp_rf, variables = "age") + - show_aggreagated_profiles(pdp_rf, size = 3) + show_aggregated_profiles(pdp_rf, size = 3) \donttest{ library("randomForest") @@ -69,7 +69,7 @@ head(pdp_rf) plot(cp_rf, variables = "age") + show_observations(cp_rf, variables = "age") + show_rugs(cp_rf, variables = "age", color = "red") + - show_aggreagated_profiles(pdp_rf, size = 3) + show_aggregated_profiles(pdp_rf, size = 3) plot(pdp_rf, variables = "age", color = "grey") diff --git a/tests/testthat/test_cluster_profiles.R b/tests/testthat/test_cluster_profiles.R index 4b9bf5a8..78ce7d5e 100644 --- a/tests/testthat/test_cluster_profiles.R +++ b/tests/testthat/test_cluster_profiles.R @@ -25,10 +25,10 @@ test_that("plot cluster_profiles",{ expect_true("aggregated_profiles_explainer" %in% class(clust_rf)) pl1 <- plot(clust_rf, color = "_label_") + - show_aggreagated_profiles(pdp_rf, color = "black", size = 3) + show_aggregated_profiles(pdp_rf, color = "black", size = 3) pl2 <- plot(cp_rf, color = "grey", variables = "Age") + - show_aggreagated_profiles(clust_rf, color = "_label_", size = 2) + show_aggregated_profiles(clust_rf, color = "_label_", size = 2) pl3 <- plot(cp_rf, variables = "Embarked", only_numerical = FALSE) diff --git a/tests/testthat/test_single_variable.R b/tests/testthat/test_single_variable.R index b3bae038..ad5cfdec 100644 --- a/tests/testthat/test_single_variable.R +++ b/tests/testthat/test_single_variable.R @@ -9,7 +9,7 @@ test_that("test plot",{ pl <- plot(cp_rf, variables = "Age") + show_observations(cp_rf, variables = "Age") + show_rugs(cp_rf, variables = "Age", color = "red") + - show_aggreagated_profiles(pdp_rf, size = 2) + show_aggregated_profiles(pdp_rf, size = 2) expect_true("gg" %in% class(pl)) }) diff --git a/vignettes/vignette_titanic.Rmd b/vignettes/vignette_titanic.Rmd index 41b6a0bf..cc3f50ad 100644 --- a/vignettes/vignette_titanic.Rmd +++ b/vignettes/vignette_titanic.Rmd @@ -142,7 +142,7 @@ sp_rf <- ceteris_paribus(explain_titanic_rf, passangers) clust_rf <- cluster_profiles(sp_rf, k = 3) head(clust_rf) plot(sp_rf, alpha = 0.1) + - show_aggreagated_profiles(clust_rf, color = "_label_", size = 2) + show_aggregated_profiles(clust_rf, color = "_label_", size = 2) ```