confusionMatrixFor_Neg1_0_1: Confusion matrix for categories -1, 0, 1 (the output of...
In heuristica: Heuristics Including Take the Best and Unit-Weight Linear

Description Usage Arguments Value References Examples

Measuring accuracy of predicting categories, where in the predictPair paradigm the categories are the relative ranks of a pair of rows. The categories are: -1 means Row1 < Row2 0 means the rows are equal or guess 1 means Row1 > Row2

1	confusionMatrixFor_Neg1_0_1(ref_data, predicted_data)

`ref_data`	A vector with outcome categories from a reference source to be predicted (e.g. the output of correctGreater.)
`predicted_data`	A vector with outcome categories from a prediction source that is trying to match ref_data (e.g. ttbModel predictions).

A 3x3 matrix of counts. Rows are outcomes of the reference data. Columns are outcomes of predicted data.

Wikipedia's entry on https://en.wikipedia.org/wiki/Confusion_matrix.

# Example 1
# Below, the correct outcome is always 1, so only the last row of the
# confusion matrix has non-zero counts.  But the predictor makes a few
# mistakes, so some non-zero counts are off the diagonal.
confusionMatrixFor_Neg1_0_1(c(1,1,1), c(1,-1,-1))
# outputs:
#    -1 0 1
# -1  0 0 0
# 0   0 0 0
# 1   2 0 1
#
# Example 2
# The prediction always matches the reference outcome, so all non-zero
# counts are on the diagonal.
confusionMatrixFor_Neg1_0_1(c(1,1,0,0,-1,-1), c(1,1,0,0,-1,-1))
# outputs:
#    -1 0 1
# -1  2 0 0
# 0   0 2 0
# 1   0 0 2
#