heuristics {heuristica}

Wrap fitted heuristics to pass to rowPairApply to call predictPair.

Description

One or more fitted heuristics can be passed in. They must all have the same cols_to_fit. If they differ on cols_to_fit, then group them in separate heuristics functions.

Usage

heuristics(...)

Arguments

...

A list of predictPairInternal implementers, e.g. a fitted ttb model.

Details

Users will generally not use the output directly but instead pass this to rowPairApply.

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 heuristics is normally passed in to.

heuristicsList for a version of this function where you can control the function called (not necessarily predictPairInternal).

predictPairInternal which must be implemented by heuristics in order to use them with the heuristics() wrapper function. This only matters for people implementing their own heuristics.

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, heuristics(ttb))
# 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, heuristics(ttb, unit))
# 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, heuristics(ttb), heuristics(unit))
# This outputs predictions with column names 'ttbModel' and
# 'ttb3'.