Description Usage Arguments Details Value Author(s) References See Also Examples
Calulate d' for ABX paradigm either assuming a differencing strategy or independent observations
1  dprime.ABX(Hits, FA, zdiff, Pc.unb, method = "diff")

Hits 
numeric in [0, 1] corresponding to Hit rate 
FA 
numeric in [0, 1] corresponding to False alarm rate 
zdiff 
numeric. Difference of zscores for Hit and False Alarm rates 
Pc.unb 
numeric in [0, 1]. Proportion correct for an unbiased observer,

method 
character. Specifies the model to describe the observer's criterion for dividing up the decision space, must be either "diff" for a differencing strategy (the default) or "IO" for independent observations. 
Two different strategies have been described for how the
observer partitions the decision space in the ABX
paradigm, either based on Independent Observations of
each stimulus or on a differencing strategy. The differecing
strategy is the default. d' can be calculated
either from the H
and FA
rates, from the difference of
zscores or from the probability correct of an unbiased observer.
Returns the value of d'
Kenneth Knoblauch
MacMillan, N. A. and Creeman, C. D. (1991) Detection Theory: A User's Guide Cambridge University Press
Green, D. M. and Swets, J. A. (1966) Signal Detection Theory and Psychophysics Robert E. Krieger Publishing Company
dprime.mAFC
, dprime.SD
,
dprime.oddity
1 2 3 4  dprime.ABX(H = 0.75, F = 0.3)
dprime.ABX(H = 0.75, F = 0.3, method = "IO")
dprime.ABX(zdiff = qnorm(0.75)  qnorm(0.3))
dprime.ABX(Pc.unb = pnorm( (qnorm(0.75)  qnorm(0.3))/2 ))

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