# Construct a 2 hidden states DCMM for the pewee data # with hidden order set to 2 and visible order set to 1. # The estimation procedure uses both the evolutionary algorithm (population size 2, # one generation) and the Bauw-Welch algorithm (one iteration). march.dcmm.construct(y=pewee,orderHC=2,orderVC=1,M=2,popSize=2,gen=1,iterBw=1,stopBw=0.0001) # Same as above, but the DCMM is replaced by a HMM (the visible order OrderVC is set to zero). HMM<-march.dcmm.construct(y=pewee,orderHC=2,orderVC=0,M=2,popSize=2,gen=1,iterBw=1,stopBw=0.0001) # A first model is computed using both EA and Baum-Welch algorithms. # The previous model is improved through two rounds of Baum-Welch optimization. models <- list() models[[length(models)+1]] <- HMM models[[length(models)+1]] <- march.dcmm.construct(y=pewee,seedModel=models[], orderVC=0,iterBw=10,stopBw=0.001) models[[length(models)+1]] <- march.dcmm.construct(y=pewee,seedModel=models[], orderVC=0,iterBw=10,stopBw=0.0001) # Show performance indicators (ll, number of independent parameters, # BIC and AIC) for all computed models. r <- do.call(rbind,lapply(models,march.summary)) print(r) # Construct a three hidden states, first-order HMM (hence OrderVC=0) for the sleep data. # By setting gen=1 and popSize=1, the estimation procedure uses only the Baum-Welch algorithm. HMM <- march.dcmm.construct(pewee,orderHC=1,orderVC=0,M=2,gen=1,popSize=1,iterBw=10,stopBw=0.0001)
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