R Community - I am attempting to fit a model as described in Hampton, Bossaerts, and O'doherty (J. Neuroscience) 2006. They use a bayesian hidden markov model to model the Reversal Learning data. I have tried using HMM and depmixS4 with no success. My data is a Reversal Learning Task in which there are 3 sets of patterns over 3 blocks. The participant receives incorrect or correct feedback. 20% of the time they receive false feedback (they are told incorrect when they were in fact correct). Once the person achieves the criterion of 9/10 correct responses the contigencies reverse. I am confused on how to set up my states, symbols, starting probabilities, transition probabilities, and emission probabilities in R. This is what I have so far.
hmm <- initHMM(c("stay", "switch"), c("correct", "incorrect"), c(.5, .5), matrix(c(.9, .1, .1, .9),2), matrix(c(.2, .8, .8, .2), 2)) dat$test <- ifelse(dat$Slide1_ACC == 0, "incorrect", "correct") viterbi(hmm, dat$test) The sequence of observations I run through the model is the feedback the participant receives. Any help would be greatly appreciated. I think what I want to do is run the model on each participant to generate the most probable path and then compare that to the actual path to see if they match up. Best, -- Edward H. Patzelt Research Assistant TRiCAM Lab University of Minnesota Psychology/Psychiatry VA Medical Center Office: S355 Elliot Hall - Twin Cities Campus Phone: 612-626-0072 Email: patze...@umn.edu Please consider the environment before printing this email www.psych.umn.edu/research/tricam [[alternative HTML version deleted]]
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