plot.explore_df: Plot Counterfactual Predictions

In healthcareai: Tools for Healthcare Machine Learning

Plot Counterfactual Predictions

Description

Plot Counterfactual Predictions

Usage

## S3 method for class '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)
# 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)

# 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)

# And you can further modify the plot like any other ggplot object
p <- plot(counterfactuals, n_use = 1, print = FALSE)
p +
  ylab("predicted number of pregnancies") +
  theme_classic() +
  theme(aspect.ratio = 1,
        panel.background = element_rect(fill = "slateblue"),
        plot.caption = element_text(face = "italic"))

healthcareai documentation built on Sept. 5, 2022, 5:12 p.m.