NEWS.md
plot.FFTrees() now has a truth.labels argument which, if set, distinguishes labels of true (signal vs. noise) cases from decision outcomes.plot.FFTrees() now has a grayscale argument which, if TRUE, creates a grayscale plot.cost_cues_default from 0 to 1, so that default cue costs correspond to mcu.@aliases FFTrees-package to documentation of main FFTrees() function.data_old folder.parsnip and tidymodels to create and evaluate these models.FFTrees version 2.0.0 was released on CRAN [on 2023-06-06]. This version adds functionality, improves consistency, and increases robustness.
Changes since last release:
get_fft_df, read_fft_df, write_fft_df, add_fft_df
add_nodes, drop_nodes, edit_nodes, flip_exits, reorder_nodes, select_nodes
stopping.rule = "statdelta"
fftrees_grow_fan() that prevented ifan algorithm from stopping when finding a perfect FFT (given the current goal.chase parameter)NA values) in data:
NA values in categorical (i.e., character/factor/logical) predictors are treated as <NA> factor levelsNA values in numeric predictors are either ignored (by default) or imputed (as the mean of the corresponding predictor) when creating and using FFTs to decide/predict (if possible)NA values in the criterion variable are yet to be dealt withget_best_tree() retrieves the ID of the best tree in an FFTrees object (given goal)get_exit_type() converts a vector of exit descriptions into FFT exits (given exit_types)get_fft_df() retrieves the tree definitions of an FFTrees objectprint.FFTrees()).quiet a list with four options).my.tree).FFTrees.guide()).exit_types as global constant.The current development version of FFTrees is available at https://github.com/ndphillips/FFTrees.
FFTrees version 1.9.0 was released on CRAN [on 2023-02-08]. Apart from adding functionality and fixing minor bugs, this version improves consistency, robustness, and transparency.
Changes since last release:
my.goal on cue and tree levels (as defined by my.goal.fun).dprime on cue and tree levels (by using "dprime" as goal.threshold, goal.chase, or goal values).my.tree).summary.FFTrees() function:
"cost" occurs in goals).dprime values in cue level statistics (x$cues$thresholds and x$cues$stats).dprime values in competition statistics (x$competition$train and x$competition$test).util_gfft.R).fft_node_sep).asif_results (in fftrees_grow_fan()).rounding argument of FFTrees().FFTrees() and fftrees_create()) by functionality.util_const.R).README.FFTrees version 1.8.0 was released on CRAN [on 2023-01-06]. This version mostly extends and improves existing functionality.
Changes since last release:
plot.FFTrees():
n.per.icon legend when what = 'icontree'.Trimmed white space from elements in tree definitions (in fftrees_apply.R).
Added check that cues occur in current data (in verify_all_cues_in_data()).
FFTrees version 1.7.5 was released on CRAN [on 2022-09-15]. This version contains mostly bug fixes, but also improves and revises existing functionality.
Changes since last release:
Added distinctions between FFTs that “decide” vs. “predict” by using corresponding labels in plots and verbal descriptions.
Improved plotting and printing FFTs (with plot.FFTrees() and print.FFTrees()):
what = 'all' vs. what = 'tree' and what = 'icontree').data.col, font, adj) to text of panel titles.FFTrees object x (to allow re-assigning to global x when using new test data).Added wacc to measures computed for competing algorithms.
Plotting with plot.FFTrees():
main argument.stats argument.helper_plot.R.FFTrees version 1.7.0 was released on CRAN [on 2022-08-31]. This version contains numerous bug fixes and improves or revises existing functionality.
Changes since last release:
print.FFTrees():
data argument to print an FFT’s training performance (by default) or prediction performance (when test data is available).tree to "best.train" or "best.test" (as when plotting FFTs).bacc or wacc in Accuracy section (and sens.w, if deviating from the default of 0.50).plot.FFTrees():
what = 'ROC' analogous to what = 'cues'.bacc or wacc in Accuracy section (and sens.w value, if deviating from the default of 0.50).tree to "best.train" or "best.test".showcues():
x as cue ranking criterion (rather than always using wacc).sens.w value when goal == 'wacc'.top < 10.data argument (as FFTrees objects only contain cue training data).alt.goal argument (to allow ranking cue accuracies by alternative goals).quiet argument (to hide feedback messages).summary.FFTrees():
definitions and stats (as a list).my.tree or fftrees_wordstofftrees().my.tree or fftrees_wordstofftrees().store.data argument of FFTrees().FFTrees version 1.6.6 was released on CRAN [on 2022-07-18].
Changes since last release:
plot.FFTrees() to not display plots properly.plot.FFTrees() no longer saves graphic params changed in par().plot.FFTRrees(): When test = 'best.test' and no test data are provided, the information text is no returned with message() rather than print().plot.FFTrees() are now returned as warnings, not messages.cost.cues and cost.outcomes are now specified as named lists to avoid confusion.goal and goal.chase.Added class probability predictions with predict.FFTrees(type = "prob").
Updated print.FFTrees() to display FFT #1 ‘in words’ (from the inwords(x) function).
Added show.X arguments to plot.FFTrees() that allow you to selectively turn on or turn off elements when plotting an FFTrees object.
Added label.tree, label.performance arguments to plot.FFTrees() that allow you to specify plot (sub) labels.
Bug fixes:
FFTrees object to a new call to FFTrees().Many additional vignettes (e.g.; Accuracy Statistics and Heart Disease Tutorial) and updates to existing vignettes.
Added cost.outcomes and cost.cues to allow the user to specify specify the cost of outcomes and cues. Also added a cost statistic throughout outputs.
Added inwords(), a function that converts an FFTrees object to words.
Added my.tree argument to FFTrees() that allows the user to specify an FFT verbally.
E.g., my.tree = 'If age > 30, predict True. If sex = {m}, predict False. Otherwise, predict True'.
Added positive predictive value ppv, negative predictive value npv and balanced predictive value bpv, as primary accuracy statistics throughout.
Added support for two FFT construction algorithms from Martignon et al. (2008): "zigzag" and "max". The algorithms are contained in the file heuristic_algorithm.R and can be implemented in FFTrees() as arguments to algorithm.
Added sens.w argument to allow differential weighting of sensitivities and specificities when selecting and applying trees.
Fixed bug in calculating importance weightings from FFForest() outputs.
Changed wording of statistics throughout package: hr (hit rate) and far (false alarm rate) (based on the classification frequency values hi and fa), are now sens for sensitivity and spec for specificity (1 − far), respectively.
The rank.method argument is now deprecated. Use algorithm instead.
Added a stats argument to plot.FFTrees(). When stats = FALSE, only the tree will be plotted without reference to any statistical output.
Grouped all competitive algorithm results (regression, cart, random forests, support vector machines) to the new x.fft$comp slot rather than a separate first level list for each algorithm. Also replaced separate algorithm wrappers with one general comp_pred() wrapper function.
Added FFForest(), a function for creating forests of FFTs, and plot.FFForest(), for visualizing forests of FFTs. (This function is experimental and still in development.)
Added random forests and support vector machines for comparison in FFTrees() using the randomForest and e1071 packages.
Changed logistic regression algorithm from the default glm() version to glmnet() for a regularized version.
predict.FFTrees() now returns a vector of predictions for a specific tree rather than creating an entirely new FFTrees object.
You can now plot cue accuracies within the plot.FFTrees() function by including the plot.FFTrees(what = 'cues') argument. (This replaces the former showcues() function.)
Many cosmetic changes to plot.FFTrees() (e.g.; gray levels, more distinct classification balls). You can also control whether the results from competing algorithms are displayed or not with the comp argument.
Bug-fixes:
Trees can now use the same cue multiple times within a tree. To do this, set rank.method = "c" and repeat.cues = TRUE.
Bug-fixes:
FFTrees() now supports a single predictor (e.g.; formula = diagnosis ~ age) which previously did not work.Streamlined code to improve cohesion between functions. This may cause issues with FFTrees objects created with earlier versions of the package. They will need to be re-created.
Updated, clearer print.FFTrees() method to see important info about an FFTrees object in matrix format.
Training and testing statistics are now in separate objects (e.g., data$train vs. data$test) to avoid confusion.
Bug-fixes:
predict.FFTrees() now works much better by passing a new dataset (data.test) as a test dataset for an existing FFTrees object.mar and layout are now reset after running plot.FFTrees()
which.tree argument in plot.FFTrees() to tree to conform to blog posts.predict.FFTrees() now works better with tibble inputs.fft label to FFTrees throughout the package to avoid confusion with fast fourier transform. Thus, the main tree building function is now FFTrees() and the new tree object class is FFTrees.[File NEWS.md last updated on 2024-05-08.]