Apply an FFT to data and generate accuracy statistics

fftrees_apply applies a fast-and-frugal tree (FFT, as an FFTrees object) to a dataset (of type mydata) and generates corresponding accuracy statistics (on cue levels and for trees).

fftrees_apply is called internally by the main FFTrees function (with mydata = "train" and --- if test data exists --- mydata = "test"). Alternatively, fftrees_apply is called when predicting outcomes for new data by predict.FFTrees.

fftrees_apply(x, mydata = NULL, newdata = NULL, fin_NA_pred = "majority")

Arguments

x

An object with FFT definitions which are to be applied to current data (as an FFTrees object).

mydata

The type of data to which the FFT should be applied (as character, either "train" or "test").

newdata

New data to which an FFT should be applied (as a data frame).

fin_NA_pred

What outcome should be predicted if the final node in a tree has a cue value of NA (as character)? Valid options are:

'noise'

predict FALSE (0/left/signal) for all corresponding cases

'signal'

predict TRUE (1/right/noise) for all corresponding cases

'majority'

predict the more common criterion value (i.e., TRUE if base rate p(TRUE) > .50 in 'train' data) for all corresponding cases

'baseline'

flip a random coin that is biased by the criterion baseline p(TRUE) (in 'train' data) for all corresponding cases

'dnk'

yet ToDo: abstain from classifying / decide to 'do not know' / defer (i.e., tertium datur)

Default: fin_NA_pred = "majority".

Value

A modified FFTrees object (with lists in x$trees containing information on FFT decisions and statistics).

See also

FFTrees for creating FFTs from and applying them to data.