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).

1 2 3 4 5 6 7 |

`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.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | ```
# 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)
``` |

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