mvnHMM2: Multivariate Normal Hidden Markov Model

In SWotherspoon/MVNSeq: Normal Mixture and Hidden Markov Models

Multivariate Normal Hidden Markov Model

Description

Fit multivariate Normal 2 State Hidden Markov models by EM.

Usage

mvnHMM2(
  y,
  cl,
  formula1,
  formula2,
  data,
  min.iters = 10,
  max.iters = 50,
  tol = 0.001,
  verbose = interactive()
)

gmvnHMM2(
  y,
  cl,
  gr,
  formula1,
  formula2,
  data,
  min.iters = 10,
  max.iters = 50,
  tol = 0.001,
  verbose = interactive()
)

grmvnHMM2(
  y,
  cl,
  gr,
  formula1,
  formula2,
  data,
  min.iters = 10,
  max.iters = 100,
  tol = 0.001,
  verbose = interactive()
)

Arguments

y	the sequence of observations
cl	an integer vector allocating observations to classes
formula1	formula for the logistic model relating the transition from state 1 to state 2 to the covariates
formula2	formula for the logistic model relating the transition from state 2 to state 1 to the covariates
data	a dataframe of covariates
min.iters	minimum number of EM iterations
max.iters	maximum number of EM iterations
tol	tolerance for the log likelihood
verbose	should the log likelihood be reported.
gr	an integer vector allocating observations to groups

Details

These functions fit multivariate Normal 2 state hidden Markov models to sequences of observations, where the transition probabilities are governed by logistic regression. mvnHMM2 fits a hidden Markov model to a single sequence of observations. gmvnHMM2 and grmvnHMM2 fit separate Markov models to several groups (sequences) of observations. gmvnHMM2 fits a hidden Markov model to each group so that each state has a different mean across groups, but a common covariance. grmvnHMM2 constrains the means of the states to be Normally distributed across groups.

Value

the fitted model

SWotherspoon/MVNSeq documentation built on June 1, 2022, 10:49 p.m.