SDT {sensR} | R Documentation |
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.
SDT(tab, method = c("probit", "logit"))
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? |
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.
Rune Haubo B Christensen
MacMillan , A. N. and Creelman, C. D (2005) Detection Theory A User's Guide. Lawrence Elbaum Associates, Inc. 2nd edition.
### Design table: ## 8 "yes"-responses to no-samples ## 1 "yes"-responses to yes-samples ## 17 "no"-response to no-samples ## 24 "no"-responses to yes-samples ## Note that response-class is columnwise and true-class is rowwise. (mat <- matrix(c(8, 17, 1, 24), 2, byrow = TRUE)) SDT(mat, "logit") SDT(mat, "probit") ## compare to AnotA(): AnotA(8, 25, 1, 25) ## 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])