build_mhmm {seqHMM}

Build a Mixture Hidden Markov Model

Description

Function build_mhmm constructs a mixture hidden Markov model object of class mhmm.

Usage

build_mhmm(
  observations,
  n_states,
  transition_probs,
  emission_probs,
  initial_probs,
  formula = NULL,
  data = NULL,
  coefficients = NULL,
  cluster_names = NULL,
  state_names = NULL,
  channel_names = NULL,
  ...
)

Arguments

observations

An stslist object (see seqdef) containing the sequences, or a list of such objects (one for each channel).

n_states

A numerical vector giving the number of hidden states in each submodel (not used if starting values for model parameters are given with initial_probs, transition_probs, or emission_probs).

transition_probs

A list of matrices of transition probabilities for the submodel of each cluster.

emission_probs

A list which contains matrices of emission probabilities or a list of such objects (one for each channel) for the submodel of each cluster. Note that the matrices must have dimensions m x s where m is the number of hidden states and s is the number of unique symbols (observed states) in the data. Emission probabilities should follow the ordering of the alphabet of observations (alphabet(observations), returned as symbol_names).

initial_probs

A list which contains vectors of initial state probabilities for the submodel of each cluster.

formula

Optional formula of class formula for the mixture probabilities. Left side omitted.

data

A data frame containing the variables used in the formula. Ignored if no formula is provided.

coefficients

An optional k x l matrix of regression coefficients for time-constant covariates for mixture probabilities, where l is the number of clusters and k is the number of covariates. A logit-link is used for mixture probabilities. The first column is set to zero.

cluster_names

A vector of optional names for the clusters.

state_names

A list of optional labels for the hidden states. If NULL, the state names are taken as row names of transition matrices. If this is also NULL, numbered states are used.

channel_names

A vector of optional names for the channels.

...

Additional arguments to simulate_transition_probs.

Details

The returned model contains some attributes such as nobs and df, which define the number of observations in the model and the number of estimable model parameters, used in computing BIC. When computing nobs for a multichannel model with C channels, each observed value in a single channel amounts to 1/C observation, i.e. a fully observed time point for a single sequence amounts to one observation. For the degrees of freedom df, zero probabilities of the initial model are defined as structural zeroes.

Value

Object of class mhmm 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.

coefficients

A matrix of parameter coefficients for covariates (covariates in rows, clusters in columns).

X

Covariate values for each subject.

cluster_names

Names for clusters.

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.

n_covariates

Number of covariates.

n_clusters

Number of clusters.

References

Helske S. and Helske J. (2019). Mixture Hidden Markov Models for Sequence Data: The seqHMM Package in R, Journal of Statistical Software, 88(3), 1-32. doi:10.18637/jss.v088.i03

See Also

fit_model for fitting mixture Hidden Markov models; summary.mhmm for a summary of a MHMM; separate_mhmm for reorganizing a MHMM into a list of separate hidden Markov models; and plot.mhmm for plotting mhmm objects.

Examples

data("biofam3c")

## Building sequence objects
marr_seq <- seqdef(biofam3c$married,
  start = 15,
  alphabet = c("single", "married", "divorced")
)
child_seq <- seqdef(biofam3c$children,
  start = 15,
  alphabet = c("childless", "children")
)
left_seq <- seqdef(biofam3c$left,
  start = 15,
  alphabet = c("with parents", "left home")
)

## Choosing colors
attr(marr_seq, "cpal") <- c("#AB82FF", "#E6AB02", "#E7298A")
attr(child_seq, "cpal") <- c("#66C2A5", "#FC8D62")
attr(left_seq, "cpal") <- c("#A6CEE3", "#E31A1C")

## MHMM with random starting values, no covariates
set.seed(468)
init_mhmm_bf1 <- build_mhmm(
  observations = list(marr_seq, child_seq, left_seq),
  n_states = c(4, 4, 6),
  channel_names = c("Marriage", "Parenthood", "Residence")
)


## Starting values for emission probabilities

# Cluster 1
B1_marr <- matrix(
  c(
    0.8, 0.1, 0.1, # High probability for single
    0.8, 0.1, 0.1,
    0.3, 0.6, 0.1, # High probability for married
    0.3, 0.3, 0.4
  ), # High probability for divorced
  nrow = 4, ncol = 3, byrow = TRUE
)

B1_child <- matrix(
  c(
    0.9, 0.1, # High probability for childless
    0.9, 0.1,
    0.9, 0.1,
    0.9, 0.1
  ),
  nrow = 4, ncol = 2, byrow = TRUE
)

B1_left <- matrix(
  c(
    0.9, 0.1, # High probability for living with parents
    0.1, 0.9, # High probability for having left home
    0.1, 0.9,
    0.1, 0.9
  ),
  nrow = 4, ncol = 2, byrow = TRUE
)

# Cluster 2

B2_marr <- matrix(
  c(
    0.8, 0.1, 0.1, # High probability for single
    0.8, 0.1, 0.1,
    0.1, 0.8, 0.1, # High probability for married
    0.7, 0.2, 0.1
  ),
  nrow = 4, ncol = 3, byrow = TRUE
)

B2_child <- matrix(
  c(
    0.9, 0.1, # High probability for childless
    0.9, 0.1,
    0.9, 0.1,
    0.1, 0.9
  ),
  nrow = 4, ncol = 2, byrow = TRUE
)

B2_left <- matrix(
  c(
    0.9, 0.1, # High probability for living with parents
    0.1, 0.9,
    0.1, 0.9,
    0.1, 0.9
  ),
  nrow = 4, ncol = 2, byrow = TRUE
)

# Cluster 3
B3_marr <- matrix(
  c(
    0.8, 0.1, 0.1, # High probability for single
    0.8, 0.1, 0.1,
    0.8, 0.1, 0.1,
    0.1, 0.8, 0.1, # High probability for married
    0.3, 0.4, 0.3,
    0.1, 0.1, 0.8
  ), # High probability for divorced
  nrow = 6, ncol = 3, byrow = TRUE
)

B3_child <- matrix(
  c(
    0.9, 0.1, # High probability for childless
    0.9, 0.1,
    0.5, 0.5,
    0.5, 0.5,
    0.5, 0.5,
    0.1, 0.9
  ),
  nrow = 6, ncol = 2, byrow = TRUE
)


B3_left <- matrix(
  c(
    0.9, 0.1, # High probability for living with parents
    0.1, 0.9,
    0.5, 0.5,
    0.5, 0.5,
    0.1, 0.9,
    0.1, 0.9
  ),
  nrow = 6, ncol = 2, byrow = TRUE
)

# Starting values for transition matrices
A1 <- matrix(
  c(
    0.80, 0.16, 0.03, 0.01,
    0, 0.90, 0.07, 0.03,
    0, 0, 0.90, 0.10,
    0, 0, 0, 1
  ),
  nrow = 4, ncol = 4, byrow = TRUE
)

A2 <- matrix(
  c(
    0.80, 0.10, 0.05, 0.03, 0.01, 0.01,
    0, 0.70, 0.10, 0.10, 0.05, 0.05,
    0, 0, 0.85, 0.01, 0.10, 0.04,
    0, 0, 0, 0.90, 0.05, 0.05,
    0, 0, 0, 0, 0.90, 0.10,
    0, 0, 0, 0, 0, 1
  ),
  nrow = 6, ncol = 6, byrow = TRUE
)

# Starting values for initial state probabilities
initial_probs1 <- c(0.9, 0.07, 0.02, 0.01)
initial_probs2 <- c(0.9, 0.04, 0.03, 0.01, 0.01, 0.01)

# Birth cohort
biofam3c$covariates$cohort <- cut(biofam3c$covariates$birthyr, c(1908, 1935, 1945, 1957))
biofam3c$covariates$cohort <- factor(
  biofam3c$covariates$cohort,
  labels = c("1909-1935", "1936-1945", "1946-1957")
)

## MHMM with own starting values and covariates
init_mhmm_bf2 <- build_mhmm(
  observations = list(marr_seq, child_seq, left_seq),
  initial_probs = list(initial_probs1, initial_probs1, initial_probs2),
  transition_probs = list(A1, A1, A2),
  emission_probs = list(
    list(B1_marr, B1_child, B1_left),
    list(B2_marr, B2_child, B2_left),
    list(B3_marr, B3_child, B3_left)
  ),
  formula = ~ sex + cohort, data = biofam3c$covariates,
  cluster_names = c("Cluster 1", "Cluster 2", "Cluster 3"),
  channel_names = c("Marriage", "Parenthood", "Residence"),
  state_names = list(
    paste("State", 1:4), paste("State", 1:4),
    paste("State", 1:6)
  )
)