plotD3_shap: Plot Shap (Break Down Uncertainty) Objects in D3 with r2d3...
In pbiecek/breakDown2: Model Agnostic Instance Level Variable Attributions

Description Usage Arguments Value References Examples

Plots Shapley values.

## S3 method for class 'shap'
plotD3(
  x,
  ...,
  baseline = NA,
  max_features = 10,
  digits = 3,
  rounding_function = round,
  bar_width = 12,
  margin = 0.2,
  scale_height = FALSE,
  min_max = NA,
  vcolors = NA,
  chart_title = NA,
  time = 0,
  max_vars = NULL,
  reload = FALSE
)

`x`	an explanation created with `shap`
`...`	other parameters.
`baseline`	if numeric then veritical line will start in `baseline`.
`max_features`	maximal number of features to be included in the plot. By default it's `10`.
`digits`	number of decimal places (`round`) or significant digits (`signif`) to be used. See the `rounding_function` argument.
`rounding_function`	a function to be used for rounding numbers. This should be `signif` which keeps a specified number of significant digits or `round` (which is default) to have the same precision for all components.
`bar_width`	width of bars in px. By default it's 12px
`margin`	extend x axis domain range to adjust the plot. Usually value between 0.1 and 0.3, by default it's 0.2
`scale_height`	if `TRUE`, the height of the plot scales with window size.
`min_max`	a range of OX axis. By deafult `NA` therefore will be extracted from the contributions of `x`. But can be set to some constants, usefull if these plots are used for comparisons.
`vcolors`	If `NA` (default), DrWhy colors are used.
`chart_title`	a character. Set custom title
`time`	in ms. Set the animation length
`max_vars`	alias for the `max_features` parameter.
`reload`	Reload the plot on resize. By default it's `FALSE`.

a r2d3 object.

Explanatory Model Analysis. Explore, Explain and Examine Predictive Models. https://ema.drwhy.ai

library("DALEX")
library("iBreakDown")
set.seed(1313)
model_titanic_glm <- glm(survived ~ gender + age + fare,
                       data = titanic_imputed, family = "binomial")
explain_titanic_glm <- explain(model_titanic_glm,
                           data = titanic_imputed,
                           y = titanic_imputed$survived,
                           label = "glm")

s_glm <- shap(explain_titanic_glm, titanic_imputed[1, ])
s_glm
plotD3(s_glm)

## Not run: 
## Not run:
library("randomForest")

HR_small <- HR[2:500,]
m_rf <- randomForest(status ~. , data = HR_small)
new_observation <- HR_test[1,]
new_observation

p_fun <- function(object, newdata){predict(object, newdata=newdata, type = "prob")}

s_rf <- shap(m_rf,
             data = HR_small[,-6],
             new_observation =  new_observation,
             predict_function = p_fun)

plotD3(s_rf, time = 500)

## End(Not run)