hilbig2014 | R Documentation |
Choice frequencies of multiattribute decisions across 3 item types (Hilbig & Moshagen, 2014).
hilbig2014
A data frame 3 variables:
B1
Frequency of choosing Option B for Item Type 1
B2
Frequency of choosing Option B for Item Type 2
B3
Frequency of choosing Option B for Item Type 3
Each participant made 32 choices for each of 3 item types with four cues (with validities .9, .8, .7, and .6).
The pattern of cue values of Option A and and B was as follows:
Item Type 1: A = (1, 1, 1, -1) vs. B = (-1, 1, -1, 1)
Item Type 2: A = (1, -1, -1, -1) vs. B = (-1, 1, 1, -1)
Item Type 3: A = (1, 1, 1, -1) vs. B = (-1, 1, 1, 1)
Hilbig, B. E., & Moshagen, M. (2014). Generalized outcome-based strategy classification: Comparing deterministic and probabilistic choice models. Psychonomic Bulletin & Review, 21(6), 1431-1443. doi: 10.3758/s13423-014-0643-0
data(hilbig2014) head(hilbig2014) # validities and cue values v <- c(.9, .8, .7, .6) cueA <- matrix( c( 1, 1, 1, -1, 1, -1, -1, -1, 1, 1, 1, -1 ), ncol = 4, byrow = TRUE ) cueB <- matrix( c( -1, 1, -1, 1, -1, 1, 1, -1, -1, 1, 1, 1 ), ncol = 4, byrow = TRUE ) # get strategy predictions strategies <- c( "baseline", "WADDprob", "WADD", "TTB", "EQW", "GUESS" ) preds <- strategy_multiattribute(cueA, cueB, v, strategies) c <- c(1, rep(.5, 5)) # upper bound of probabilities # use Bayes factor for strategy classification n <- rep(32, 3) strategy_postprob(k = hilbig2014[1:5, ], n, preds)
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