# minModel: Minimalist Model In heuristica: Heuristics Including Take the Best and Unit-Weight Linear

## Description

Fit the Minimalist heuristic by specifying columns and a dataset. It searches cues in a random order, making a decision based on the first cue that discriminates (has differing values on the two objects).

## Usage

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

## 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. 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.

## Value

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

`predictPairProb` for prediction.
 ```1 2 3 4 5``` ```## Fit column (5,4) to column (1,0), having validity 1.0, and column (0,1), ## validity 0. train_matrix <- cbind(c(5,4), c(1,0), c(0,1)) min <- minModel(train_matrix, 1, c(2,3)) predictPair(oneRow(train_matrix, 1), oneRow(train_matrix, 2), min) ```