This function updates the options of a modelStudio object. WARNING: Editing default options may cause unintended behavior.

ms_update_options(object, ...)

Arguments

object

A modelStudio created with modelStudio().

...

Options to change in the form option_name = value, e.g. time = 0, facet_dim = c(1,2).

Value

An object of the r2d3, htmlwidget, modelStudio class.

Options

Main options:

scale_plot

TRUE Makes every plot the same height, ignores bar_width.

show_boxplot

TRUE Display boxplots in Feature Importance and Shapley Values plots.

show_subtitle

TRUE Should the subtitle be displayed?

subtitle

label parameter from explainer.

ms_title

Title of the dashboard.

ms_subtitle

Subtitle of the dashboard (makes space between the title and line).

ms_margin_*

Dashboard margins. Change margin_top for more ms_subtitle space.

margin_*

Plot margins. Change margin_left for longer/shorter axis labels.

w

420 in px. Inner plot width.

h

280 in px. Inner plot height.

bar_width

16 in px. Default width of bars for all plots, ignored when scale_plot = TRUE.

line_size

2 in px. Default width of lines for all plots.

point_size

3 in px. Default point radius for all plots.

[bar,line,point]_color

[#46bac2,#46bac2,#371ea3]

positive_color

#8bdcbe for Break Down and Shapley Values bars.

negative_color

#f05a71 for Break Down and Shapley Values bars.

default_color

#371ea3 for Break Down bar and highlighted line.

Plot-specific options:

** is a two letter code unique to each plot, might be one of [bd,sv,cp,fi,pd,ad,rv,fd,tv,at].

**_title

Plot-specific title. Default varies.

**_subtitle

Plot-specific subtitle. Default is subtitle.

**_axis_title

Plot-specific axis title. Default varies.

**_bar_width

Plot-specific width of bars. Default is bar_width, ignored when scale_plot = TRUE.

**_line_size

Plot-specific width of lines. Default is line_size.

**_point_size

Plot-specific point radius. Default is point_size.

**_*_color

Plot-specific [bar,line,point] color. Default is [bar,line,point]_color.

References

  • The input object is implemented in DALEX

  • Feature Importance, Ceteris Paribus, Partial Dependence and Accumulated Dependence explanations are implemented in ingredients

  • Break Down and Shapley Values explanations are implemented in iBreakDown

Examples

library("DALEX")
library("modelStudio")

# fit a model
model_titanic <- glm(survived ~., data = titanic_imputed, family = "binomial")

# create an explainer for the model
explainer_titanic <- explain(model_titanic,
                             data = titanic_imputed,
                             y = titanic_imputed$survived)
#> Preparation of a new explainer is initiated
#>   -> model label       :  lm  (  default  )
#>   -> data              :  2207  rows  8  cols 
#>   -> target variable   :  2207  values 
#>   -> predict function  :  yhat.glm  will be used (  default  )
#>   -> predicted values  :  No value for predict function target column. (  default  )
#>   -> model_info        :  package stats , ver. 4.2.0 , task classification (  default  ) 
#>   -> predicted values  :  numerical, min =  0.008128381 , mean =  0.3221568 , max =  0.9731431  
#>   -> residual function :  difference between y and yhat (  default  )
#>   -> residuals         :  numerical, min =  -0.9628583 , mean =  -2.569729e-10 , max =  0.9663346  
#>   A new explainer has been created!  

# make a studio for the model
ms <- modelStudio(explainer_titanic,
                  N = 200,  B = 5) # faster example
#> `new_observation` argument is NULL. `new_observation_n` observations needed to calculate local explanations are taken from the data.

# update the options
new_ms <- ms_update_options(ms,
                            time = 0,
                            facet_dim = c(1,2),
                            margin_left = 150)
new_ms