Plot Counterfactual Predictions

# S3 method for explore_df
plot(
  x,
  n_use = 2,
  aggregate_fun = median,
  reorder_categories = TRUE,
  x_var,
  color_var,
  jitter_y = TRUE,
  sig_fig = 3,
  font_size = 11,
  strip_font_size = 0.85,
  line_width = 0.5,
  line_alpha = 0.7,
  rotate_x = FALSE,
  nrows = 1,
  title = NULL,
  caption,
  print = TRUE,
  ...
)

Arguments

x	A explore_df object from `explore`
n_use	Number of features to vary, default = 4. If the number of features varied in `explore` is greater than `n_use`, additional features will be aggregated over by `aggregate_fun`
aggregate_fun	Default = median. Varying features in x are mapped to the x-axis, line color, and vertical- and horizontal facets. If more than four features vary, this function is used to aggreagate across the least-important varying features.
reorder_categories	If TRUE (default) varying categorical features are arranged by their median predicted outcome. If FALSE, the incoming level orders are retained, which is alphabetical by default, but you can set your own level orders with `reorder`
x_var	Feature to put on the x-axis (unquoted). If not provided, the most important feature is used, with numerics prioritized if one varies
color_var	Feature to color lines (unquoted). If not provided, the most important feature excluding `x_var` is used.
jitter_y	If TRUE (default) and a feature is mapped to color (i.e. if there is more than one varying feature), the vertical location of the lines will be jittered slightly (no more than 1 avoid overlap.
sig_fig	Number of significant figures (digits) to use in labels of numeric features. Default = 3; set to Inf to not truncate decimals.
font_size	Parent font size for the plot. Default = 11
strip_font_size	Relative font size for facet strip title font. Default = 0.85
line_width	Width of lines. Default = 0.5
line_alpha	Opacity of lines. Default = 0.7
rotate_x	If FALSE (default), x axis tick labels are positioned horizontally. If TRUE, they are rotated one quarter turn, which can be helpful when a categorical feature with long labels is mapped to x.
nrows	Only used when the number of varying features is three. The number of rows into which the facets will be arranged. Default = 1. NULL lets the number be determined algorithmically
title	Plot title
caption	Plot caption. Defaults to model used to make counterfactual predictions. Can be a string for custom caption or NULL for no caption.
print	Print the plot? Default is FALSE. Either way, the plot is invisibly returned
...	Not used

Value

ggplot object, invisibly

Examples

# First, we need a model
set.seed(4956)
m <- machine_learn(pima_diabetes, patient_id, outcome = pregnancies,
                   models = "rf", tune = FALSE)
#> Training new data prep recipe...
#> Variable(s) ignored in prep_data won't be used to tune models: patient_id
#> 
#> pregnancies looks numeric, so training regression algorithms.
#> 
#> After data processing, models are being trained on 14 features with 768 observations.
#> Based on n_folds = 5 and hyperparameter settings, the following number of models will be trained: 5 rf's 
#> Training at fixed values: Random Forest
#> 
#> *** Models successfully trained. The model object contains the training data minus ignored ID columns. ***
#> *** If there was PHI in training data, normal PHI protocols apply to the model object. ***
# Then we can explore our model through counterfactual predictions
counterfactuals <- explore(m)

# By default only the two most important varying features are plotted. This
# example shows how counterfactual predictions can provide insight into how a
# model maps inputs (features) to the output (outcome). This plot shows that for
# this dataset, age is the most important predictor of the number of pregnancies
# a woman has had, and the predicted number of pregnancies rises basically
# linearly from approximately 20 to 40 and then levels off.
plot(counterfactuals)
#> With 4 varying features and n_use = 2, using median to aggregate predicted outcomes across skinfold and weight_class. You could turn `n_use` up to see the impact of more features.

# To see the effects of more features in the model, increase the value of
# `n_use`. You can also specify which of the varying features are mapped to the
# x-axis and the color scale, and you can customize a variety of plot attributes
plot(counterfactuals, n_use = 3, x_var = weight_class, color_var = age,
    font_size = 9, strip_font_size = 1, line_width = 2, line_alpha = .5,
    rotate_x = TRUE, nrows = 1)
#> With 4 varying features and n_use = 3, using median to aggregate predicted outcomes across weight_class. You could turn `n_use` up to see the impact of more features.

# And you can further modify the plot like any other ggplot object
p <- plot(counterfactuals, n_use = 1, print = FALSE)
#> With 4 varying features and n_use = 1, using median to aggregate predicted outcomes across diastolic_bp, skinfold, and weight_class. You could turn `n_use` up to see the impact of more features.
p +
  ylab("predicted number of pregnancies") +
  theme_classic() +
  theme(aspect.ratio = 1,
        panel.background = element_rect(fill = "slateblue"),
        plot.caption = element_text(face = "italic"))