Plot an FFTrees object

Source: R/plotFFTrees_function.R

plot.FFTrees visualizes an FFTrees object created by the FFTrees function.

plot.FFTrees is the main plotting function of the FFTrees package and called when evaluating the generic plot on an FFTrees object.

plot.FFTrees visualizes a selected FFT, key data characteristics, and various aspects of classification performance.

As x may not contain test data, plot.FFTrees by default plots the performance characteristics for training data (i.e., fitting), rather than for test data (i.e., for prediction). When test data is available, specifying data = "test" plots prediction performance.

Whenever the sensitivity weight (sens.w) is set to its default of sens.w = 0.50, a level shows balanced accuracy (bacc). If, however, sens.w deviates from its default, the level shows the tree's weighted accuracy value (wacc) and the current sens.w value (below the level).

Many aspects of the plot (e.g., its panels) and the FFT's appearance (e.g., labels of its nodes and exits) can be customized by setting corresponding arguments.

# S3 method for FFTrees
plot(
  x = NULL,
  data = "train",
  what = "all",
  tree = 1,
  main = NULL,
  cue.labels = NULL,
  decision.labels = NULL,
  truth.labels = NULL,
  cue.cex = NULL,
  threshold.cex = NULL,
  decision.cex = 1,
  comp = TRUE,
  show.header = NULL,
  show.tree = NULL,
  show.confusion = NULL,
  show.levels = NULL,
  show.roc = NULL,
  show.icons = NULL,
  show.iconguide = NULL,
  hlines = TRUE,
  label.tree = NULL,
  label.performance = NULL,
  n.per.icon = NULL,
  level.type = "bar",
  which.tree = NULL,
  decision.names = NULL,
  stats = NULL,
  grayscale = FALSE,
  ...
)

Arguments

x

An FFTrees object created by the FFTrees function.

data

The type of data in x to be plotted (as a string) or a test dataset (as a data frame).

  • A valid data string must be either 'train' (for fitting performance) or 'test' (for prediction performance).

  • For a valid data frame, the specified tree is evaluated and plotted for this data (as 'test' data), but the global FFTrees object x remains unchanged unless it is re-assigned.

By default, data = 'train' (as x may not contain test data).

what

What should be plotted (as a character string)? Valid options are:

'all'

Plot the tree diagram with all corresponding guides and performance statistics, but excluding cue accuracies.

'cues'

Plot only the marginal accuracy of cues in ROC space. Note that cue accuracies are not shown when calling what = 'all' and use the showcues function.

'icontree'

Plot tree diagram with icon arrays on exit nodes. Consider also setting n.per.icon and show.iconguide.

'tree'

Plot only the tree diagram.

'roc'

Plot only the performance of tree(s) (and comparison algorithms) in ROC space.

Default: what = 'all'.

tree

The tree to be plotted (as an integer, only valid when the corresponding tree argument is non-empty). Default: tree = 1. To plot the best training or best test tree with respect to the goal specified during FFT construction, use 'best.train' or 'best.test', respectively.

main

The main plot label (as a character string).

cue.labels

An optional string of labels for the cues / nodes (as character vector).

decision.labels

A character vector of length 2 indicating the content-specific names for noise vs. signal predictions/exits.

truth.labels

A character vector of length 2 indicating the content-specific names for true noise vs. signal cases (using `decision.labels` if unspecified).

cue.cex

The size of the cue labels (as numeric).

threshold.cex

The size of the threshold labels (as numeric).

decision.cex

The size of the decision labels (as numeric).

comp

Should the performance of competitive algorithms (e.g.; logistic regression, random forests, etc.) be shown in the ROC plot (if available, as logical)?

show.header

Show header with basic data properties (in top panel, as logical)?

show.tree

Show nodes and exits of FFT (in middle panel, as logical)?

show.confusion

Show a 2x2 confusion matrix (in bottom panel, as logical)?

show.levels

Show performance levels (in bottom panel, as logical)?

show.roc

Show ROC curve (in bottom panel, as logical)?

show.icons

Show exit cases as icon arrays (in middle panel, as logical)?

show.iconguide

Show icon guide (in middle panel, as logical)?

hlines

Show horizontal panel separation lines (as logical)? Default: hlines = TRUE.

label.tree

A label for the FFT (optional, as character string).

label.performance

A label for the performance section (optional, as character string).

n.per.icon

The number of cases represented by each icon (as numeric).

level.type

The type of performance levels to be drawn at the bottom (as character string, either "bar" or "line". Default: level.type = "bar".

which.tree

Deprecated argument. Use tree instead.

decision.names

Deprecated argument. Use decision.labels instead.

stats

Deprecated argument. Should statistical information be plotted (as logical)? Use what = "all" to include performance statistics and what = "tree" to plot only a tree diagram.

grayscale

logical. If TRUE, the plot is shown in grayscale.

...

Graphical parameters (passed to text of panel titles, to showcues when what = 'cues', or to title when what = 'roc').

Value

An invisible FFTrees object x

and a plot visualizing and describing an FFT (as side effect).

See also

showcues for plotting cue accuracies; print.FFTrees for printing FFTs; summary.FFTrees for summarizing FFTs; FFTrees for creating FFTs from and applying them to data.

Other plot functions: showcues()

Examples

# Create FFTs (for heartdisease data):
heart_fft <- FFTrees(formula = diagnosis ~ .,
                     data = heart.train)
#> ✔ Created an FFTrees object.
#>   Ranking 13 cues:  ■■■■■■■■                          23% | ETA:  1s
#>   Ranking 13 cues:  ■■■■■■■■■■■■■■■■■                 54% | ETA:  0s
#>   Ranking 13 cues:  ■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■  100% | ETA:  0s
#> 
#> ✔ Ranked 13 cues (optimizing 'bacc').
#> ✔ Created 7 FFTs with 'ifan' algorithm (chasing 'bacc').
#> ✔ Defined 7 FFTs.
#> ✔ Applied 7 FFTs to 'train' data.
#> ✔ Ranked 7 FFTs by 'train' data.
#> ✔ Expressed 7 FFTs in words.

# Visualize the default FFT (Tree #1, what = 'all'):
plot(heart_fft, main = "Heart disease",
     decision.labels = c("Absent", "Present"))


# Visualize cue accuracies (in ROC space):
plot(heart_fft, what = "cues",  main = "Cue accuracies for heart disease data")
#> Plotting cue training statistics:
#> — Cue accuracies ranked by bacc
#> 


# Visualize tree diagram with icon arrays on exit nodes:
plot(heart_fft, what = "icontree", n.per.icon = 2,
     main = "Diagnosing heart disease")


# Visualize performance comparison in ROC space:
plot(heart_fft, what = "roc", main = "Performance comparison for heart disease data")


# Visualize predictions of FFT #2 (for new test data) with custom options:
plot(heart_fft, tree = 2, data = heart.test,
     main = "Predicting heart disease",
     cue.labels = c("1. thal?", "2. cp?", "3. ca?", "4. exang"),
     decision.labels = c("ok", "treat"), truth.labels = c("Healthy", "Sick"),
     n.per.icon = 2,
     show.header = TRUE, show.confusion = TRUE, show.levels = TRUE, show.roc = TRUE,
     hlines = FALSE, font = 3, col = "steelblue")
#> Applying FFTrees object x to new test data...
#> ✔ Applied 7 FFTs to 'test' data.
#> Success, but re-assign output to x or use fftrees_apply() to globally change x


# # For details, see
# vignette("FFTrees_plot", package = "FFTrees")