rowPairApply: Apply functions to all row pairs.
In heuristica: Heuristics Including Take the Best and Unit-Weight Linear

Description Usage Arguments Value See Also Examples

Apply functions like heuristic predictions to all row pairs in a matrix or data.frame. This does not accept arbitrary functions– they must be functions designed to be run by rowPairApply.

1	rowPairApply(test_data, ...)

`test_data`	The data to apply the functions to as a matrix or data.frame. Heuristics must have already been fitted to trying data and must include the same criterion_col and cols_to_fit.
`...`	The functions that generate the functions to apply, such as heuristics(ttb) and correctGreater(col)– see example below.

A matrix of outputs from the functions. The number of rows is based on the number of row pairs in test_data. If the input has N rows, the output will have N x (N-1) rows. The number of columns will be at least the number of functions but may be more as some functions may output more than one column.

heuristics and heuristics to wrap heuristics to be applied.

rowIndexes to get apply to output row indexes for the pair.

correctGreater to get the correct output based on the criterion column. (CorrectGreater should be used with heuristics while probGreater should be used with heuristicsProb.)

## Fit two models to data.
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:ncol(data)))
lreg <- logRegModel(data, 1, c(2:ncol(data)))

## Generate predictions for all row pairs for these two models:
rowPairApply(data, heuristics(ttb, lreg))
## Returns a matrix of 2 columns, named ttbModel and regModel, and 6 rows.
## The original data had 4 rows, meaning there are 4*3/2 = 6 row pairs.

## To see which row pair is which row, use rowIndexes:
rowPairApply(data, rowIndexes(), heuristics(ttb, lreg))
## Returns a matrix with columns Row1, Row2, ttbModel, logRegModel.
## (RowIndexes returns *two* columns.)

## To see whether the first row was actually greater than the second in the
## row pair, use correctGreater and give it the criterion column index, in
## this case 1.
rowPairApply(data, heuristics(lreg, ttb), correctGreater(1))
## Returns a matrix with columns logRegModel, ttbModel,
## CorrectGreater.  Values are -1, 0, or 1.

## To do the same analysis for the *probabilty* that the first row is
## greater. use heuristicsProb and probGreater.  Warning: Not all heuristica
## models have implemented the prob greater function.
rowPairApply(data, heuristicsProb(lreg, ttb), probGreater(1))
## Returns a matrix with columns logRegModel, ttbModel, ProbGreater.
## Values range from 0.0 to 1.0.