heuristicsList {heuristica}

Wrapper for fitted heuristics to generate predictions with rowPairApply.

Description

A list of fitted heuristics are passed in. They must all implement the fn function passed in, and they must all have the same cols_to_fit. If they differ on these, then group them in separate heuristicsLists.

Usage

heuristicsList(list_of_fitted_heuristics, fn)

Arguments

list_of_fitted_heuristics

Normally a list of predictProbInternal implementers, e.g. a fitted ttb model.

fn

The function to be called on the heuristics, which is typically predictPairInternal (or the experimental function predictProbInternal) but can be any function with the signature function(object, row1, row2) that is implemented by the heuristics in list_of_fitted_heuristics.

Details

Users will generally not use the output directly– instead just pass this into one of the rowPairApply functions.

Value

An object of class heuristics, which implements createFunction. Users will generally not use this directly– rowPairApply will.

See Also

rowPairApply which is what the output of heuristicsList is normally passed in to.

heuristics for a simpler version of this function with more examples. It is recommended for most uses. (It is hard-coded for fn=predictPairInternal, which is what most people use.)

heuristicsProb for a version of this function tailored for predictProbInternal rather than predictPairInternal.

Examples

# Use one fitted ttbModel with column 1 as criterion and columns 2,3 as
# cues.
data <- cbind(y=c(30,20,10,5), x1=c(1,1,0,0), x2=c(1,1,0,1))
ttb <- ttbModel(data, 1, c(2:3))
rowPairApply(data, heuristicsList(list(ttb), predictPairInternal))
# This outputs ttb's predictions for all 6 row pairs of data.
# (It has 6 row pairs because 4*2/2 = 6.)  It gets the predictions
# by calling ttb's predictPairInternal.

# Use the same fitted ttbModel plus a unit weight model with the same
# criterion and cues.
unit <- unitWeightModel(data, 1, c(2,3))
rowPairApply(data, heuristicsList(list(ttb, unit), predictPairInternal))
# This outputs predictions with column names 'ttbModel' and
# 'unitWeightLinearModel'.

# Use the same fitted ttbModel plus another ttbModel that has different
# cols_to_fit.  This has to be put in a separate heuristicsList function.
ttb_just_col_3 <- ttbModel(data, 1, c(3), fit_name="ttb3")
rowPairApply(data, heuristicsList(list(ttb), predictPairInternal),
  heuristicsList(list(ttb_just_col_3), predictPairInternal))
# This outputs predictions with column names 'ttbModel' and
# 'ttb3'.