build_mm | R Documentation |
Function build_mm
builds and automatically estimates a Markov model. It is also a shortcut for
constructing a Markov model as a restricted case of an hmm
object.
build_mm(observations)
observations |
An |
Unlike the other build functions in seqHMM
, the build_mm
function
automatically estimates the model parameters. In case of no missing values,
initial and transition probabilities are
directly estimated from the observed initial state probabilities and transition counts.
In case of missing values, the EM algorithm is run once.
Note that it is possible that the data contains a symbol from which there are no transitions anywhere (even to itself), which would lead to a row in transition matrix full of zeros. In this case the 'build_mm' (as well as the EM algorithm) assumes that the the state is absorbing in a way that probability of staying in this state is 1.
Object of class hmm
with following elements:
observations
State sequence object or a list of such containing the data.
transition_probs
A matrix of transition probabilities.
emission_probs
A matrix or a list of matrices of emission probabilities.
initial_probs
A vector of initial probabilities.
state_names
Names for hidden states.
symbol_names
Names for observed states.
channel_names
Names for channels of sequence data.
length_of_sequences
(Maximum) length of sequences.
n_sequences
Number of sequences.
n_symbols
Number of observed states (in each channel).
n_states
Number of hidden states.
n_channels
Number of channels.
plot.hmm
for plotting the model.
# Construct sequence data
data("mvad", package = "TraMineR")
mvad_alphabet <-
c("employment", "FE", "HE", "joblessness", "school", "training")
mvad_labels <- c(
"employment", "further education", "higher education",
"joblessness", "school", "training"
)
mvad_scodes <- c("EM", "FE", "HE", "JL", "SC", "TR")
mvad_seq <- seqdef(mvad, 17:86,
alphabet = mvad_alphabet,
states = mvad_scodes, labels = mvad_labels, xtstep = 6
)
# Define a color palette for the sequence data
attr(mvad_seq, "cpal") <- colorpalette[[6]]
# Estimate the Markov model
mm_mvad <- build_mm(observations = mvad_seq)
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