heuristica (version 1.0.3)

ttbGreedyModel: Greedy Take The Best

Description

A variant of the Take The Best heuristic with a different cue order, namely using conditional cue validity, where the validity of a cue is judged only on row pairs not already decided by prior cues. Specifically, it uses the cue ranks returned by conditionalCueValidityComplete.

Usage

ttbGreedyModel(
  train_data,
  criterion_col,
  cols_to_fit,
  fit_name = "ttbGreedyModel"
)

Arguments

train_data

Training/fitting data as a matrix or data.frame.

criterion_col

The index of the column in train_data that has the criterion.

cols_to_fit

A vector of column indices in train_data, used to fit the criterion.

fit_name

Optional The name other functions can use to label output. It defaults to the class name. It is useful to change this to a unique name if you are making multiple fits, e.g. "ttb1", "ttb2", "ttbNoReverse."

Value

An object of class ttbGreedyModel, which can be passed in to predictPair.

References

Martignon, L., & Hoffrage, U. (2002). Fast, frugal, and fit: Simple heuristics for paired comparisons. Theory and Decision, 52: 29-71.

See Also

conditionalCueValidityComplete for the metric used to sort cues.

ttbModel for the original version of Take The Best.

predictPair for predicting whether row1 is greater.

predictPairProb for predicting the probability row1 is greater.

Examples

# NOT RUN {
## A data set where Take the Best and Greedy Take the Best disagree.
matrix <- cbind(y=c(3:1), x1=c(1,0,0), x2=c(1,0,1))
ttb <- ttbModel(matrix, 1, c(2,3))
ttb$cue_validities
# Returns
#  x1  x2 
# 1.0 0.5
ttbG <- ttbGreedyModel(matrix, 1, c(2:3))
ttbG$cue_validities
# Returns
#  x1  x2 
#   1   1
# because after using x1, only decisions between row 2 and 3 are left,
# and x2 gets 100% right  on those (after reversal).  However, these
# cue_validities depend on using x1, first, so cue_rank is key.
ttbG$cue_ranks
# Returns
#  x1  x2 
#   1   2

# Now see how this affects predictions on row 2 vs. 3.
# Take the best guesses (output 0).
predictPair(oneRow(matrix, 2), oneRow(matrix, 3), ttb)
# Greedy Take The Best selects row 2 (output 1).
predictPair(oneRow(matrix, 2), oneRow(matrix, 3), ttbG)

# }