Examples of FFTs with FFTrees | Mushrooms data | Creating FFTs | Visualizing cue accuracies | Visualizing FFT performance | Trading off prediction errors | An alternative FFT | Iris.v data | Viewing alternative FFTs | Titanic data | Vignettes | References

1. Using my.tree | Verbally defining FFTs | Example | Inspect FFTrees object: | Creating an alternative FFT | Visualize FFT: | FFT definition: | Note the flipped direction value for 2nd cue (exit = '0'): | 'if (cp = a), predict 1' in the tree definition corresponds to | 'if (cp != a), predict 0' in the my.tree description and plot. | 2. Using tree.definitions | Overview: Retrieving, manipulating, and using FFT definitions | Obtaining individual tree definitions | Manipulating individual tree definitions | Using magrittr pipes to combine steps | Re-converting and collecting sets of tree definitions | Applying sets of tree definitions to data | Details | Vignettes

Visualizing FFTrees | The Titanic data | Visualising cue accuracies | Additional arguments | Plotting performance for new data | Vignettes

R package | Textbooks | Change of hosting service and URLs | The ds4psy and i2ds books, courses, and R package | Installation | Resources | About | Citation | License

Motivation: A problem of probabilities | Information provided and asked | Using Bayes' theorem | Using natural frequencies | Using riskyr | Risk vs. uncertainty | Promoting risk perception and communication | A. A fancy calculator | B. Translating between formats | Computing frequencies from probabilities | Computing probabilities from frequencies | Switching back and forth | All at once: Defining a riskyr scenario | C. Visualizing relationships between formats and variables | Icon array | Tree diagram | Area plot | Bar plot | Prism plot | Alternative perspectives | References | Resources | Contact | All riskyr vignettes

Basics | Adopting two perspectives on a population | Example | Accuracy as a third perspective | Avoiding common sources of confusion | Accuracy metrics | A. Specific metrics: Conditional probabilities | B. General metrics: Measures of accuracy | 1. Overall accuracy acc | 2. Weighted accuracy wacc | 3. Matthews correlation coefficient mcc | 4. F1 score | References | Resources | Contact | All riskyr vignettes

Two representational formats | Frequencies | Definitions | Perspectives: Basic vs. combined frequencies | Relationships among frequencies | Probabilities | Non-conditional vs. conditional probabilities | Relationships among probabilities | Probabilities as ratios between frequencies | Practice | An example | Your tasks | References | Resources | Contact | All riskyr vignettes

Functions | Curves as a function of prevalence | Planes as a function of sensitivity and specificity (given a prevalence) | Resources | Contact | All riskyr vignettes

Getting started | Defining a scenario | Example | Necessary scenario information | Optional scenario information | Viewing scenario information | Prism plot | Practice | Icon array | Alternative perspectives | Using existing scenarios | 1. Selecting a scenario | 2. Printing scenario information | 3. Visualizing frequencies and probabilities | 4. Visualizing probabilistic relationships | Conclusion | References | Resources | Contact | All riskyr vignettes

Step\ 1: Install and load the FFTrees package | Step\ 2: Create FFTs from training data (and test on testing data) | Other arguments | Step\ 3: Inspect and summarize FFTs | Step\ 4: Visualise the final FFT | Advanced functions | Accessing outputs | Predicting for new data | Directly defining FFTs | Vignettes | References

Overview | Illustration | Your color palettes | Vignettes

Creating new color palettes | 1. Defining colors | Defining colors from their RGB values | Defining colors from their HEX values | 2. Combining colors into a palette | Comparing color palettes | Call for contributions | Resources | Vignettes

Accuracy Statistics in FFTrees | Conditional accuracy statistics | Aggregate accuracy statistics | Speed and frugality statistics | Additional measures | Vignettes | References

Details on the FFTrees() function | Example: Predicting heart disease | Creating trees with FFTrees() | Elements of an FFTrees object | Basic performance characteristics of FFTs | Cue performance information | Tree definitions | Accuracy statistics of FFTs | Classification decisions | Predicting new cases with predict() | Visualising trees | Manually defining an FFT | Vignettes | References

Fast-and-Frugal Trees (FFTs) | Using the FFTrees package | Getting started | Vignettes | Datasets | Details about the datasets | Citing FFTrees | Contact | Bibliography

Using colors in base R | Using colors in ggplot2 | Resources | Vignettes

Using colors in base R | Using colors in ggplot2 | Viewing and comparing color palettes | Finding similar colors | Creating new color palettes | Finding colors by name | Getting shades of a color | Creating color gradients | Resources | Vignettes

Overview | Viewing colors and color palettes with seecol() | A. Viewing details of a single color palette | B. Viewing and comparing multiple color palettes | Other seecol() arguments | Using a color palette with usecol() | Customizing color palettes | Selecting subsets | Extending color palettes | Mixing colors and color palettes | Controlling transparency | Creating and comparing custom palettes | Finding colors | Finding similar colors with simcol() | Finding color names with grepal() | Auxiliary color functions | Adjusting transparency with ac() | Creating color gradients with shades_of() | Defining color palettes with newpal() | Illustrating color palettes with demopal() | Resources | Vignettes

Overview | Plotting empty slides and boxes | Colors | Dimensions | Plotting styled text | Mark text | Underline text | Post text | Headings | URLs | Resources | Vignettes