Tutorial: Creating FFTs for heart disease

Nathaniel Phillips and Hansjörg Neth


Tutorial: Creating FFTs for heart disease

This tutorial on using the FFTrees package follows the examples presented in Phillips et al. (2017) (freely available in html | PDF):

In the following, we explain how to use FFTrees to create, evaluate and visualize FFTs in four simple steps.

Step 1: Install and load the FFTrees package

We can install FFTrees from CRAN using install.packages(). (We only need to do this once.)

# Install the package from CRAN:

To use the package, we first need to load it into your current R session. We load the package using library():

# Load the package:

The FFTrees package contains several vignettes that guide through the package’s functionality (like this one). To open the main guide, run FFTrees.guide():

# Open the main package guide: 

Step 2: Create FFTs from training data (and test on testing data)

In this example, we will create FFTs from a heart disease data set. The training data are in an object called heart.train, and the testing data are in an object called heart.test. For these data, we will predict diagnosis, a binary criterion that indicates whether each patent has or does not have heart disease (i.e., is at high-risk or low-risk).

To create an FFTrees object, we use the function FFTrees() with two main arguments:

  1. formula expects a formula indicating a binary criterion variable as a function of one or more predictor variable(s) to be considered for the tree. The shorthand formula = diagnosis ~ . means to include all predictor variables.

  2. data specifies the training data used to construct the FFTs (which must include the criterion variable).

Here is how we can construct our first FFTs:

# Create an FFTrees object:
heart.fft <- FFTrees(formula = diagnosis ~ .,           # Criterion and (all) predictors
                     data = heart.train,                # Training data
                     data.test = heart.test,            # Testing data
                     main = "Heart Disease",            # General label
                     decision.labels = c("Low-Risk", "High-Risk")  # Decision labels (False/True)
#> Aiming to create a new FFTrees object:
#> — Setting 'goal = bacc'
#> — Setting 'goal.chase = bacc'
#> — Setting 'goal.threshold = bacc'
#> — Setting 'max.levels = 4'
#> — Using default 'cost.outcomes' = (0 1 1 0)
#> — Using default 'cost.cues' = (0 per cue)
#> Successfully created a new FFTrees object.
#> Aiming to define FFTs:
#> Aiming to create FFTs with 'ifan' algorithm (chasing 'bacc'):
#> Aiming to rank 13 cues (optimizing 'bacc'):
#> Successfully ranked 13 cues.
#> Successfully created 7 FFTs with 'ifan' algorithm.
#> Successfully defined 7 FFTs.
#> Aiming to apply FFTs to 'train' data:
#> Successfully applied FFTs to 'train' data.
#> Aiming to rank FFTs by 'train' data:
#> Successfully ranked FFTs by 'train' data.
#> Aiming to apply FFTs to 'test' data:
#> Successfully applied FFTs to 'test' data.
#> Aiming to express FFTs in words:
#> Successfully expressed FFTs in words.
#> Aiming to fit comparative algorithms (disable by do.comp = FALSE):
#> Successfully fitted comparative algorithms.

Evaluating this expression runs code that examines the data, optimizes thresholds based on our current goals for each cue, and creates and evaluates 7 FFTs. The resulting FFTrees object that contains the tree definitions, their decisions, and their performance statistics, are assigned to the heart.fft object.

Other arguments

  • algorithm: There are two different algorithms available to build FFTs "ifan" (Phillips et al., 2017) and "dfan" (Phillips et al., 2017). ("max" (Martignon et al., 2008), and "zigzag" (Martignon et al., 2008) are no longer supported).

  • max.levels: Changes the maximum number of levels that are allowed in the tree.

The following arguments apply when using the “ifan” or “dfan” algorithms for creating new FFTs:

  • goal.chase: The goal.chase argument changes which statistic is maximized during tree construction (for the "ifan" and "dfan" algorithms). Possible arguments include "acc", "bacc", "wacc", "dprime", and "cost". The default is "wacc" with a sensitivity weight of 0.50 (which renders it identical to "bacc").

  • goal: The goal argument changes which statistic is maximized when selecting trees after construction (for the "ifan" and "dfan" algorithms). Possible arguments include "acc", "bacc", "wacc", "dprime", and "cost".

  • my.tree or tree.definitions: We can define a new tree from a verbal description (as a set of sentences), or manually specify sets of FFTs as a data frame (in appropriate format). See the Manually specifying FFTs vignette for details.

Step 3: Inspect and summarize FFTs

Now we can inspect and summarize the generated decision trees. We will start by printing the FFTrees object to return basic information to the console:

# Print an FFTrees object:
#> Heart Disease
#> FFTrees 
#> - Trees: 7 fast-and-frugal trees predicting diagnosis
#> - Outcome costs: [hi = 0, fa = 1, mi = 1, cr = 0]
#> FFT #1: Definition
#> [1] If thal = {rd,fd}, decide High-Risk.
#> [2] If cp != {a}, decide Low-Risk.
#> [3] If ca > 0, decide High-Risk, otherwise, decide Low-Risk.
#> FFT #1: Training Accuracy
#> Training data: N = 150, Pos (+) = 66 (44%) 
#> |          | True + | True - | Totals:
#> |----------|--------|--------|
#> | Decide + | hi  54 | fa  18 |      72
#> | Decide - | mi  12 | cr  66 |      78
#> |----------|--------|--------|
#>   Totals:        66       84   N = 150
#> acc  = 80.0%   ppv  = 75.0%   npv  = 84.6%
#> bacc = 80.2%   sens = 81.8%   spec = 78.6%
#> FFT #1: Training Speed, Frugality, and Cost
#> mcu = 1.74,  pci = 0.87,  E(cost) = 0.200

The output tells us several pieces of information:

All statistics to evaluate each tree can be derived from a 2 x 2 confusion table:

**Table 1**: A 2x2 confusion table illustrating the types of frequency counts for 4 possible outcomes.

Table 1: A 2x2 confusion table illustrating the types of frequency counts for 4 possible outcomes.

For definitions of all accuracy statistics, see the accuracy statistics vignette.

Step 4: Visualise the final FFT

Use plot() to visualize an FFT (an FFTrees object):

# Plot predictions of the best FFT when applied to test data:
plot(heart.fft,      # An FFTrees object
     data = "test")  # data to use (i.e., either "train" or "test")?

Other arguments

  • tree: Which tree in the object should beplotted? To plot a tree other than the best fitting tree (FFT #1), just specify another tree as an integer (e.g.; plot(heart.fft, tree = 2)).

  • data: For which dataset should statistics be shown? Either data = "train" (showing fitting or “Training” performance by default), or data = "test" (showing prediction or “Testing” performance).

  • stats: Should accuracy statistics be shown with the tree? To show only the tree, without any performance statistics, include the argument stats = FALSE.

# Plot only the tree, without accuracy statistics:
plot(heart.fft, what = "tree")

# plot(heart.fft, stats = FALSE)  #  The 'stats' argument has been deprecated.
  • comp: Should statistics from competitive algorithms be shown in the ROC curve? To remove the performance statistics of competitive algorithms (e.g.; regression, random forests), include the argument comp = FALSE.

  • what: To show individual cue accuracies (in ROC space), include the argument what = "cues":

# Plot cue accuracies (for training data) in ROC space:
plot(heart.fft, what = "cues")
#> Plotting cue training statistics:
#> — Cue accuracies ranked by bacc

See the Plotting FFTrees vignette for details on plotting FFTs.

Additional steps

Accessing outputs

An FFTrees object contains many different outputs, to see them all, run names()

# Show the names of all of the outputs in heart.fft:
#> [1] "criterion_name" "cue_names"      "formula"        "trees"         
#> [5] "data"           "params"         "competition"    "cues"

Predicting for new data

To predict classifications for a new dataset, use the standard predict() function. For example, here’s how to predict the classifications for data in the heartdisease object (which actually is just a combination of heart.train and heart.test):

# Predict classifications for a new dataset:
        newdata = heartdisease)

Defining FFTs in words

To define a specific FFT and apply it to data, we can define a tree by providing its verbal description to the my.tree argument:

# Create an FFT manually (from description):
my.heart.fft <- FFTrees(formula = diagnosis ~.,
                        data = heart.train,
                        data.test = heart.test,
                        main = "My Heart FFT",
                        my.tree = "If chol > 350, predict True. 
                                   If cp != {a}, predict False. 
                                   If age <= 35, predict False, otherwise, predict True.")

Running this code evaluates my.tree for the specified sets of data. A visualization of the resulting tree shows its performance summary (for the training data):

plot(my.heart.fft, data = "train")

The resulting tree is actually not too bad, although its first node is pretty useless (as it only classifies 3 cases, all as false alarms). Thus, omitting the first node will result in an even simpler FFT that cannot be worse. Feel free to verify this — and see the Manually specifying FFTs vignette for additional details on defining FFTs from verbal or abstract descriptions.


Here is a complete list of the vignettes available in the FFTrees package:

Vignette Description
Main guide: FFTrees overview An overview of the FFTrees package
1 Tutorial: FFTs for heart disease An example of using FFTrees() to model heart disease diagnosis
2 Accuracy statistics Definitions of accuracy statistics used throughout the package
3 Creating FFTs with FFTrees() Details on the main FFTrees() function
4 Manually specifying FFTs How to directly create FFTs without using the built-in algorithms
5 Visualizing FFTs Plotting FFTrees objects, from full trees to icon arrays
6 Examples of FFTs Examples of FFTs from different datasets contained in the package


Martignon, L., Katsikopoulos, K. V., & Woike, J. K. (2008). Categorization with limited resources: A family of simple heuristics. Journal of Mathematical Psychology, 52(6), 352–361. https://doi.org/10.1016/j.jmp.2008.04.003
Phillips, N. D., Neth, H., Woike, J. K., & Gaissmaier, W. (2017). FFTrees: A toolbox to create, visualize, and evaluate fast-and-frugal decision trees. Judgment and Decision Making, 12(4), 344–368. https://doi.org/10.1017/S1930297500006239