singleCueModel: Single Cue Model
In heuristica: Heuristics Including Take the Best and Unit-Weight Linear

Description Usage Arguments Details Value See Also Examples

Create a single cue model by specifying columns and a dataset. It sorts cues in order of cueValidity and uses the cue with the highest cueValidity. If the cue does not discriminate it guesses randomly. If several cues have the highest validity, then on each prediction it randomly selects which one to use (so it might not give the same answer every time).

singleCueModel(
  train_data,
  criterion_col,
  cols_to_fit,
  reverse_cues = TRUE,
  fit_name = "singleCueModel"
)

`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.

This single cue model follows the definition used in this reference: Hogarth, R. & Karelaia, N. (2007). Heuristic and Linear Models of Judgment: Matching Rules and Environments. Psychological Review. 114(3), pp.733-758. Note that other researchers have sometimes used other measures than cue validity to select the single cue to be used.

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

predictPairProb for prediction.

##Fit column (5,4) to column (1,0), having validity 1.0, and column (0,1),
## validity 0.
train_matrix <- cbind(y=c(5,4), x1=c(1,0), x2=c(0,1))
singlecue <- singleCueModel(train_matrix, 1, c(2,3))
predictPair(oneRow(train_matrix, 1), oneRow(train_matrix, 2), singlecue)