SDT: Signal Detection Theory Computation of d-prime In sensR: Thurstonian Models for Sensory Discrimination

Description

The function computes d-prime for any 2 x J table where J >= 2 for the "yes–no" or "A-Not A" experiment using the Signal Detection Theory (SDT) algorithm to compute J-1 d-prime's. The algorithm is also called the "empirical probit transform". The function also provides the "logit" counterpart.

Usage

 1 SDT(tab, method = c("probit", "logit"))

Arguments

 tab A 2 x J table with true class relation in rows (only two true classes) and the J-class response in columns method should the empirical probit or logit transform be computed?

Value

A (J-1) x 3 matrix. The first two columns contains the z-transform of the Hit rate and the False Alarm rate respectively—ready to plot along with the empirical ROC curve. The third column contains the estimated d-primes.

Author(s)

Rune Haubo B Christensen

References

MacMillan , A. N. and Creelman, C. D (2005) Detection Theory A User's Guide. Lawrence Elbaum Associates, Inc. 2nd edition.

Examples

 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 ### Design table: ## 8 "yes"-responses to yes-samples ## 1 "yes"-responses to no-samples ## 17 "no"-response to yes-samples ## 24 "no"-responses to no-samples ## Note that response-class is columnwise and true-class is rowwise: (mat <- rbind(c(8, 17), c(1, 24))) SDT(mat, "logit") SDT(mat, "probit") ## compare to AnotA(): m1 <- AnotA(8, 25, 1, 25) m1 ## Compute d-prime 'by hand': ## Hit rate and False alarm rates: H <- 8/(8+17) FA <- 1/(1+24) zH <- qnorm(H) zFA <- qnorm(FA) ## d-prime: zH - zFA # d' ## Multi-response-class example (odor example from MacMillan and ## Creelman, 2005) (odor <- matrix(c(112, 112, 72, 53, 22, 4, 7, 38, 50, 117, 101, 62), 2, byrow = TRUE)) obj <- SDT(odor) ROC(obj[3,3])

sensR documentation built on May 2, 2019, 9:43 a.m.