Description Usage Arguments Value References See Also

Unit-weight linear model inspired by Robyn Dawes.
Unit Weight Model assigns unit (+1 or -1) weights based on
`cueValidity`

.

A cue validity > 0.5 results in a weight of +1.

A cue validity < 0.5 results in a weight of -1.

This version differs from others in that it uses a weight of 0 if cue validity is 0.5 (rather than randomly assigning +1 or -1) to give faster convergence of average accuracy.

1 2 3 4 5 6 7 | ```
unitWeightModel(
train_data,
criterion_col,
cols_to_fit,
reverse_cues = TRUE,
fit_name = "unitWeightModel"
)
``` |

`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. |

`fit_name` |
Optional The name other functions can use to label output. It defaults to the class name. |

An object of `class`

unitWeightModel. This is a list
containing at least the following components:

"cue_validities": A list of cue validities for the cues in order of cols_to_fit.

"linear_coef": A list of linear model coefficients (-1 or +1) for the cues in order of cols_to_fit. (It can only return -1's if reverse_cues=TRUE.)

Wikipedia's entry on https://en.wikipedia.org/wiki/Unit-weighted_regression.

`cueValidity`

for the metric used to to determine cue direction.

`predictPair`

for predicting whether row1 is greater.

`predictPairProb`

for predicting the probability row1 is
greater.

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