Description Usage Arguments Value References Examples
State decoding for the HSMM estimated using pmleHSMM
. Decoding is based on the Viterbi algorithm and the corresponding HMM model representation.
1 | decodeHSMM(y, mod)
|
y |
vector containing the observed time series. |
mod |
model object as returned by |
Returns a vector containing the decoded states.
For more details about the Viterbi algorithm, see for example:
Zucchini, W., MacDonald, I.L. and Langrock, R. (2016): Hidden Markov models for time series: An introduction using R. 2nd edition. Chapman & Hall/CRC, Boca Raton.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 | # running this example might take a few minutes
#
# 1.) 2-state gamma-HSMM for hourly muskox step length
# with an unstructured start of length of 10
#
# initial values
p_list0<-list()
p_list0[[1]]<-c(dgeom(0:9,0.2),1-pgeom(9,0.2))
p_list0[[2]]<-c(dgeom(0:9,0.2),1-pgeom(9,0.2))
mu0<-c(5,150)
sigma0<-c(3,180)
#
# fit 2-state gamma-HSMM with lambda=c(100,100)
# and difference order 3
# estimation might take a few minutes
PHSMM<-pmleHSMM(y=muskox$step,N=2,p_list=p_list0,mu=mu0,
sigma=sigma0,lambda=c(100,100),order_diff=3,
y_dist='gamma')
#
# state decoding
s_HSMM<-decodeHSMM(muskox$step,mod=PHSMM)
# plot sequence of the decoded time series
plot(muskox$step[1:1000],type='h',xlab='time (h)',ylab='step (m)',
main='',col=s_HSMM)
legend('topright',c('state 1','state 2'),lwd=2,col=1:2)
# running this example might take a few minutes
#
# 2.) 3-state gamma-HSMM for hourly muskox step length
# with an unstructured start of length of 10
#
# initial values
p_list0<-list()
p_list0[[1]]<-c(dgeom(0:9,0.2),1-pgeom(9,0.2))
p_list0[[2]]<-c(dgeom(0:9,0.2),1-pgeom(9,0.2))
p_list0[[3]]<-c(dgeom(0:9,0.2),1-pgeom(9,0.2))
omega0<-matrix(0.5,3,3)
diag(omega0)<-0
mu0<-c(5,100,350)
sigma0<-c(3,90,300)
#
# fit 3-state gamma-HSMM with lambda=c(1000,1000,1000)
# and difference order 3
# estimation might take some minutes
PHSMM<-pmleHSMM(y=muskox$step,N=3,p_list=p_list0,mu=mu0,
sigma=sigma0,omega=omega0,
lambda=c(1000,1000,1000),
order_diff=3,y_dist='gamma')
#
# state decoding
s_HSMM<-decodeHSMM(muskox$step,mod=PHSMM)
# plot sequence of the decoded time series
plot(muskox$step[1:1000],type='h',xlab='time (h)',ylab='step (m)',
main='',col=s_HSMM)
legend('topright',c('state 1','state 2', 'state 3'),lwd=2,col=1:3)
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