statesDistributionsPlot: Probability Density Functions of the States
In HMMCont: Hidden Markov Model for Continuous Observations Processes

Description Usage Arguments Value Author(s) See Also Examples

The function plots the Gaussian probability density functions from the means and variances of the whole data set, the two sub-sets corresponding to the two Markov chain states, and additionally from the HMM model (i.e. the means and variances taken form the last Baum-Welch iteration).

1	statesDistributionsPlot(hmm, sc = 1)

`hmm`	An object of the class ContObservHMM.
`sc`	Scaling factor used when the initial HMM-object was set.

Plot of the probability density functions.

Mikhail A. Beketov

Functions: hmmsetcont, baumwelchcont, and viterbicont.

Returns<-logreturns(Prices) # Getting a stationary process
Returns<-Returns*10 		# Scaling the values
hmm<-hmmsetcont(Returns) 	# Creating a HMM object
for(i in 1:6){hmm<-baumwelchcont(hmm)} # Baum-Welch is 
# executed 6 times and results are accumulated
hmmcomplete<-viterbicont(hmm) # Viterbi execution

statesDistributionsPlot(hmmcomplete, sc=10) # PDFs of 
# the whole data set and two states are plotted