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...ppreciated? library(HMM) ## DECLARE PARAMETERS OF THE KNOWN MODEL states = c(1,2,3) symbols = c(1,2) startProb = c(0.5,0.25,0.25) transProb = matrix(c(0.8,0.05,0.15,0.2,0.6,0.2,0.2,0.3,0.5),3,3,TRUE) emissionProb = matrix(c(0.9,0.1,0.2,0.8,0.7,0.3), 3,2,TRUE) # CREATE THE KNOWN MODEL hmmTrue = initHMM(states, symbols, startProb, transProb , emissionProb) # SIMULATE 1000 OBSERVATIONS OF THE KNOWN MODEL observation = simHMM(hmmTrue, 1000) obs = observation$observation #ESTIMATE A MODEL USING THE OBSERVATIONS GENERATED FROM THE KNOWN MODEL hmmInit = initHMM(states, symbols, c(1/3,1/3,1/3)) hmmFit...

...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 observation...