heuristica (version 1.0.3)

percentCorrectList: Percent correct of a list of heuristics' predictPair on test_data.

Description

Returns overall percent correct for all heuristics. 1. Create predictions using predictPair for all row pairs for all fitted heuristics in the list. 2. Calculate percent correct for each heuristic. Assumes the heuristics passed in have already been fitted to training data and all have the same criterion column.

Usage

percentCorrectList(test_data, fitted_heuristic_list)

Arguments

test_data

Data to try to predict. Must have same criterion column and cols_to_fit as the data heuristics were fit to.

fitted_heuristic_list

A list of one or more heuristics fitted to data, e.g. the output of ttbModel.

Value

A one-row data.frame of numbers from 0 to 100, the percent correc of each heuristic. Each column is named with the heuristic's class or the fit name.

See Also

percentCorrectList for a version which takes heuristics as parameters rather than wrapped in a list.

Examples

# NOT RUN {
df <- data.frame(y=c(30,20,10,5), name=c("a", "b", "c", "d"),
                 x1=c(1,1,0,0), x2=c(1,1,0,1))
ttb <- ttbModel(df, 1, c(3:4))
sing <- singleCueModel(df, 1, c(3:4))
percentCorrectList(df, list(ttb, sing))
#    ttbModel singleCueModel
#  1     0.75      0.8333333
# TTB gets 75% correct while single cue model gets 83%.

# Now repeatedly sample 2 rows of the data set and see how outcomes are
# affected, tracking with the fit_name.
set.seed(1) # If you want to reproduce the same output as below.
ttb1 <- ttbModel(df[sample(nrow(df), 2),], 1, c(3:4), fit_name="fit1")
ttb2 <- ttbModel(df[sample(nrow(df), 2),], 1, c(3:4), fit_name="fit2")
ttb3 <- ttbModel(df[sample(nrow(df), 2),], 1, c(3:4), fit_name="fit3")
percentCorrectList(df, list(ttb1, ttb2, ttb3))
#        fit1 fit2 fit3
# 1 0.8333333 0.75 0.75

# }