# unitWeightModel: Unit-weight linear model In heuristica: Heuristics Including Take the Best and Unit-Weight Linear

## Description

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.

## Usage

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

## 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. `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.

## Value

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.)

## References

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.