# Tutorial: Creating FFTs for heart disease

#### Nathaniel Phillips and Hansjörg Neth

#### 2025-09-03

## 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):

- 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

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:
    install.packages("FFTrees")

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

    # Load the package:
    library(FFTrees)

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:
    FFTrees.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 patient 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)
                         )

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.fft

    #> Heart Disease
    #> FFTrees
    #> - Trees: 7 fast-and-frugal trees predicting diagnosis
    #> - Cost of outcomes:  hi = 0,  fa = 1,  mi = 1,  cr = 0
    #> - Cost of cues:
    #>      age      sex       cp trestbps     chol      fbs  restecg  thalach
    #>        1        1        1        1        1        1        1        1
    #>    exang  oldpeak    slope       ca     thal
    #>        1        1        1        1        1
    #>
    #> 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
    #> cost_dec = 0.200,  cost_cue = 1.740,  cost = 1.940

The output tells us several pieces of information:

- The tree with the highest weighted sensitivity `wacc` with a sensitivity weight of 0.5 is selected as the best tree.

- Here, the best tree, FFT #1 uses three cues: `thal`, `cp`, and `ca`.

- Several summary statistics for this tree in training and test data are summarized.

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.

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

### Step 4: Visualise the final FFT

We use `plot(x)` to visualize an FFT (from an `FFTrees` object `x`). Using `data = "train"` evaluates an FFT for training data (fitting), whereas `data = "test"` predicts the performance of an FFT for a different dataset:

    # 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

The `plot()` function for `FFTrees` object

- `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`: Which parts of an `FFTrees` object should be visualized (e.g., `all`, `icontree` and `tree`). Using `what = "roc"` plots tree performance as an ROC curve. To show individual cue accuracies (in ROC space), specify `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.

### Advanced functions

Creating sets of FFTs and evaluating them on data by printing and plotting individual FFTs provides the core functionality of FFTrees. However, the package also provides more advanced functions for accessing, defining, using and evaluating FFTs.

#### Accessing outputs

An `FFTrees` object contains many different outputs. Basic performance information on the current data and set of FFTs is available by the `summary()` function. To see and access parts of an `FFTrees` object, use `str()` or `names()`:

    # Show the names of all outputs in heart.fft:
    names(heart.fft)

    #> [1] "criterion_name" "cue_names"      "formula"        "trees"
    #> [5] "data"           "params"         "competition"    "cues"

Key elements of an `FFTrees` object are explained in the vignette on Creating FFTs with FFTrees().

#### Predicting for new data

To predict classification outcomes for new data, 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:
    predict(heart.fft,
            newdata = heartdisease)

#### Directly defining FFTs

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. Similarly, we can define sets of FFT definitions (as a data frame) and evaluate them on data by using the `tree.definitions` argument of `FFTrees()`. As we often start from an existing set of FFTs, FFTrees provides a set of functions for extracting, converting, and modifying tree definitions.

See the vignette on Manually specifying FFTs for defining FFTs from descriptions and modifying tree definitions.

## Vignettes

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

## References

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