bayesianStevensPowerLaw: bayesianStevensPowerLaw

In dlandy/WarpedBayes: Implements Design Pattern Discussed in Landy, Crawford, & Corbin

Description

bayesianStevensPowerLaw

Usage

bayesianStevensPowerLaw(stimuli, kappaObjective = 1,
  kappa = psiLog(kappaObjective), tauStimuli = 1, tauCategory = 1,
  responses = NULL, mode = "prediction", responseGrid = NULL)

Arguments

stimuli	a vector of stimuli, between 0 and inf
kappaObjective	An alternative specification giving kappa in objective units
kappa	The location of the category
tauStimuli	The precision of the stimulus traces: may be a single number or a vector
tauCategory	The precision of the category distribution
responses	an optional vector of responses.
responseGrid	an optional vector of response structured Responses If responses are given, the return value is the logLikelihood of the responses given the parameters

Value

A vector the transformed stimuli

See Also

bayesianHuttenlocherSpatialMemory

Examples

bayesianStevensPowerLaw(1:100)
bayesianStevensPowerLaw(1:100, kappa=1, tauStimuli=2)
bayesianStevensPowerLaw(1:100, kappa=1, tauStimuli=2, responses=2*(1:100)^.9)

dlandy/WarpedBayes documentation built on May 29, 2019, 2:49 p.m.