logRegModel: Logistic Regression model using cue differences as predictors
In heuristica: Heuristics Including Take the Best and Unit-Weight Linear

Description Usage Arguments Details Value

Create a logistic regression model by specifying columns and a dataset. It fits the model with R's glm function.

logRegModel(
  train_data,
  criterion_col,
  cols_to_fit,
  cue_order_fn = rankByCueValidity,
  suppress_warnings = TRUE,
  fit_name = "logRegModel"
)

`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.
`cue_order_fn`	Optional argument as a function that orders cues. This only matters for overspecified models (e.g. too many cues for the number of rows), in which case it affects which cues are dropped. The rightmost cues in the order are dropped first, so the function rankByCueValidity means cues with the lowest cueValidity in the training set will be be dropped first. The function must have the signature function(train_data, criterion_col, cols_to_fit).
`suppress_warnings`	Optional argument specifying whether glm warnings should be suppressed or not. Default is TRUE.
`fit_name`	Optional The name other functions can use to label output. It defaults to the class name.

This version assumes you do not want to include the intercept.

For a discussion of how logistic regression works, see: https://www.r-bloggers.com/what-does-a-generalized-linear-model-do/ Note that our criterion is the probability that row 1 is greater than row 2 when a pair is encountered.

An object of class logRegModel.

heuristica documentation built on Sept. 8, 2021, 9:08 a.m.