Description Usage Arguments Details Value See Also Examples

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.

1 | ```
build_mm(observations)
``` |

`observations` |
An |

Unlike the other build functions in `seqHMM`

, the `build_mm`

function
automatically estimates the model parameters. As initial and transition probabilities can be
directly estimated from the observed initial state probabilities and transition counts, there
is no need for starting values or further estimation with the `fit_model`

function.

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.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | ```
# 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)
``` |

seqHMM documentation built on Nov. 6, 2018, 5:07 p.m.

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