mps {hmm.discnp} | R Documentation |
Calculates the most probable hidden state underlying each observation.
mps(y, object = NULL, tpm, Rho, ispd, yval = NULL)
y |
The observations for which the underlying most probable hidden states are required. May be a sequence of observations, or a matrix each column of which constitutes a (replicate) sequence of observations. |
object |
An object describing a fitted hidden Markov
model, as returned by hmm() . In order to
make any kind of sense, object should bear some
reasonable relationship to y . |
tpm |
The transition probability matrix for a hidden
Markov model; ignored if object is non-null. Should
bear some reasonable relationship to y . |
Rho |
A matrix specifying the probability distributions
of the observations for a hidden
Markov model; ignored if object is non-null. Should
bear some reasonable relationship to y . |
ispd |
The initial state probability distribution for a hidden
Markov model; ignored if object is non-null. Should
bear some reasonable relationship to y . |
yval |
The set of unique values of the observations;
calculated from the observations y if left NULL . |
For each t the maximum value of gamma_t(i), i.e. of the (estimated) probability that the state at time t is equal to i, is calculated, and the corresponding index returned. These indices are interpreted as the values of the (most probable) states. I.e. the states are assumed to be 1, 2, ..., K, for some K.
If y
is a single observation sequence, then the
value is a vector of corresponding most probable states.
If y
is a matrix of replicate sequences, then the value is
a matrix, the j-th column of which constitutes the vector of
most probable states underlying the j-th replicate sequence.
The sequence of most probable states as calculated by this function will not in general be the most probable sequence of states. It may not even be a possible sequence of states. This function looks at the state probabilities separately for each time t, and not at the states in their sequential context.
To obtain the most probable sequence of states use
viterbi()
.
Rolf Turner r.turner@auckland.ac.nz http://www.math.unb.ca/~rolf
Rabiner, L. R., "A tutorial on hidden Markov models and selected applications in speech recognition," Proc. IEEE vol. 77, pp. 257 – 286, 1989.
# See the help for sim.hmm() for how to generate y.sim. ## Not run: try <- hmm(y.sim,K=2,verb=TRUE) sss.1 <- mps(y.sim,try) sss.2 <- mps(y.sim,tpm=P,ispd=c(0.25,0.75),Rho=R) # P and R as in the help # for sim.hmm(). # The order of the states has gotten swapped; 3-sss.1[,1] is much # more similar to sss.2[,1] than is sss.1[,1]. ## End(Not run)