Description Usage Arguments Value See Also

Validity Weight Model is a linear model with weights calculated by
`cueValidity`

.

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

`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. |

An object of `class`

validityWeightModel. 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": Same as cue validities for this model.

`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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