hidden_paths {seqHMM} | R Documentation |
Most Probable Paths of Hidden States
Description
Function hidden_paths
computes the most probable path of
hidden states of a (mixture) hidden Markov model given the observed sequences.
Usage
hidden_paths(model, ...)
## S3 method for class 'hmm'
hidden_paths(model, as_stslist = FALSE, ...)
## S3 method for class 'mhmm'
hidden_paths(model, as_stslist = FALSE, ...)
## S3 method for class 'nhmm'
hidden_paths(model, as_stslist = FALSE, ...)
## S3 method for class 'mnhmm'
hidden_paths(model, as_stslist = FALSE, ...)
Arguments
model |
A hidden Markov model. |
... |
Ignored. |
as_stslist |
Logical. If |
Value
The most probable paths of hidden states as an data.table
.
The log-probability is included as an attribute
log_prop
.
See Also
hmm_biofam for information on the model used in the example;
and ggseqplot::ggseqiplot()
and stacked_sequence_plot()
for plotting hidden paths.
Examples
# Load a pre-defined HMM
data("hmm_biofam")
# Compute the most probable hidden state paths given the data and the model
mpp <- hidden_paths(hmm_biofam)
head(mpp)
# Plot hidden paths for the first 100 individuals
seqs <- data_to_stslist(mpp, "id", "time", "state")
stacked_sequence_plot(seqs, type = "i", ids = 1:100)
# Because the model structure is so sparse that the posterior probabilities are
# mostly peaked to single state at each time point, the joint probability of
# observations and most probable paths of hidden states is almost identical to
# log-likelihood:
sum(attr(mpp, "log_prob"))
logLik(hmm_biofam)
[Package seqHMM version 2.0.0 Index]