ttbModel: Take The Best
In heuristica: Heuristics Including Take the Best and Unit-Weight Linear

Description Usage Arguments Details Value References See Also Examples

An implementation of the Take The Best heuristic. It sorts cues in order of cueValidity, making a decision based on the first cue that discriminates (has differing values on the two objects).

ttbModel(
  train_data,
  criterion_col,
  cols_to_fit,
  reverse_cues = TRUE,
  fit_name = "ttbModel"
)

`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.
`reverse_cues`	Optional parameter to reverse cues as needed. By default, the model will reverse the cue values for cues with cue validity < 0.5, so a cue with validity 0 becomes a cue with validity 1. Set this to FALSE if you do not want that, i.e. the cue stays validity 0.
`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."

Cues that are tied in validity are sorted once at fitting time, and that order is used consistently for all predictions with that model. But re- fitting may lead to a different cue order. (An alternative would be to randomly re-order on every prediction.)

An object of class ttbModel, which can be passed to a variety of functions to make predictions, e.g. predictPair and percentCorrectList.

Gigerenzer, G. & Goldstein, D. G. (1996). "Reasoning the fast and frugal way: Models of bounded rationality". Psychological Review, 103, 650-669.

Wikipedia's entry on https://en.wikipedia.org/wiki/Take-the-best_heuristic.

cueValidity for the metric used to sort cues.

predictPair for predicting whether row1 is greater.

predictPairProb for predicting the probability row1 is greater.

percentCorrectList for the accuracy of predicting all row pairs in a matrix or data.frame.

# Fit column 1 (y) to columns 2 and 3 (x1 and x2) of train_matrix.
train_matrix <- cbind(y=c(5,4), x1=c(1,0), x2=c(0,0))
ttb <- ttbModel(train_matrix, 1, c(2,3))
# Have ttb predict whether row 1 or 2 has a greater value for y.  The
# output is 1, meaning it predicts row1 is bigger.
predictPair(oneRow(train_matrix, 1), oneRow(train_matrix, 2), ttb)

# Now ask it the reverse-- predict whther row 2 or row 1 is greater.  The
# output is -1, meaning it still predicts row1 is bigger.  (It is a
# symmetric heuristic.)
predictPair(oneRow(train_matrix, 2), oneRow(train_matrix, 1), ttb)

# But this test data results in an incorrect prediction-- that row1 has a
# smaller criterion than row2-- because x1 has a reversed direction.
test_matrix <- cbind(y=c(5,4), x1=c(0,1), x2=c(0,0))
predictPair(oneRow(test_matrix, 1), oneRow(test_matrix, 2), ttb)