seqHMM (version 1.1.0)

build_lcm: Build a Latent Class Model

Description

Function build_lcm is a shortcut for constructing a latent class model as a restricted case of an mhmm object.

Usage

build_lcm(
  observations,
  n_clusters,
  emission_probs,
  formula,
  data,
  coefficients,
  cluster_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_clusters

A scalar giving the number of clusters/submodels (not used if starting values for model parameters are given with emission_probs).

emission_probs

A matrix containing emission probabilities for each class by rows, or in case of multichannel data a list of such matrices. Note that the matrices must have dimensions k x s where k is the number of latent classes 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).

formula

Covariates as an object of class formula, left side omitted.

data

An optional data frame, list or environment containing the variables in the model. If not found in data, the variables are taken from environment(formula).

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 classes/clusters.

channel_names

A vector of optional names for the channels.

Value

Object of class mhmm with the 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.

See Also

fit_model for estimating model parameters; summary.mhmm for a summary of a mixture model; separate_mhmm for organizing an mhmm object into a list of separate hmm objects; and plot.mhmm for plotting mixture models.

Examples

# NOT RUN {
# Simulate observations from two classes
set.seed(123)
obs <- seqdef(rbind(
  matrix(sample(letters[1:3], 500, TRUE, prob = c(0.1, 0.6, 0.3)), 50, 10),
  matrix(sample(letters[1:3], 200, TRUE, prob = c(0.4, 0.4, 0.2)), 20, 10)))

# Initialize the model
set.seed(9087)
model <- build_lcm(obs, n_clusters = 2)

# Estimate model parameters
fit <- fit_model(model)

# How many of the observations were correctly classified:
sum(summary(fit$model)$most_probable_cluster == rep(c("Class 2", "Class 1"), times = c(500, 200)))

############################################################
# }
# NOT RUN {
# LCM for longitudinal data

# Define 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)
  
# Initialize the LCM with random starting values
set.seed(7654)
init_lcm_mvad1 <- build_lcm(observations = mvad_seq,
  n_clusters = 2, formula = ~male, data = mvad)

# Own starting values for emission probabilities
emiss <- rbind(rep(1/6, 6), rep(1/6, 6))

# LCM with own starting values
init_lcm_mvad2 <- build_lcm(observations = mvad_seq,
  emission_probs = emiss, formula = ~male, data = mvad)

# Estimate model parameters (EM algorithm with random restarts)
lcm_mvad <- fit_model(init_lcm_mvad1, 
  control_em = list(restart = list(times = 5)))$model

# Plot the LCM
plot(lcm_mvad, interactive = FALSE, ncol = 2)

###################################################################

# Binomial regression (comparison to glm)

require("MASS")
data("birthwt")

model <- build_lcm(
  observations = seqdef(birthwt$low), emission_probs = diag(2), 
  formula = ~age + lwt + smoke + ht, data = birthwt)
fit <- fit_model(model)
summary(fit$model)
summary(glm(low ~ age + lwt + smoke + ht, binomial, data = birthwt))


# Multinomial regression (comparison to multinom)

require("nnet")

set.seed(123)
n <- 100
X <- cbind(1, x1 = runif(n, 0, 1), x2 =  runif(n, 0, 1))
coefs <- cbind(0,c(-2, 5, -2), c(0, -2, 2))
pr <- exp(X %*% coefs)  + rnorm(n*3)
pr <- pr/rowSums(pr)
y <- apply(pr, 1, which.max)
table(y)

model <- build_lcm(
  observations = seqdef(y), emission_probs = diag(3), 
  formula = ~x1 + x2,  data = data.frame(X[, -1]))
fit <- fit_model(model)
summary(fit$model)
summary(multinom(y ~ x1 + x2, data = data.frame(X[,-1])))
# }