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).

Usage

statesDistributionsPlot(hmm, sc = 1)

Arguments

hmm

An object of the class ContObservHMM.

sc

Scaling factor used when the initial HMM-object was set.

Value

Plot of the probability density functions.

Author(s)

Mikhail A. Beketov

See Also

Functions: hmmsetcont,
baumwelchcont, and
viterbicont.

Examples

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

Results

R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> library(HMMCont)
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/HMMCont/statesDistributionsPlot.Rd_%03d_medium.png", width=480, height=480)
> ### Name: statesDistributionsPlot
> ### Title: Probability Density Functions of the States
> ### Aliases: statesDistributionsPlot
> ### Keywords: Baum-Welch Viterbi
>
> ### ** Examples
>
>
> 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
>
>
>
>
>
> dev.off()
null device
1
>