mvnMix: Multivariate Normal Mixture Models

In SWotherspoon/MVNSeq: Normal Mixture and Hidden Markov Models

Multivariate Normal Mixture Models

Description

Fit multivariate Normal mixture models by EM.

Usage

mvnMix(
  y,
  cl,
  min.iters = 10,
  max.iters = 50,
  tol = 0.001,
  verbose = interactive()
)

gmvnMix(
  y,
  cl,
  gr,
  common.fractions = FALSE,
  min.iters = 10,
  max.iters = 50,
  tol = 0.001,
  verbose = interactive()
)

grmvnMix(
  y,
  cl,
  gr,
  common.fractions = FALSE,
  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
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
common.fractions	should the mixing fractions be common across groups

Details

These functions fit K component multivariate Normal mixtures to sequences of observations. 'mvnMix' fits a mixture model to a single sequence of observations. 'gmvnMix' and 'grmvnMix' fits separate mixture models to several groups (sequences) of observations. 'gmvnMix' fits a mixture to each group so that each component has a different mean across groups, but a common covariance. 'grmvnMix' constrains the means of the components to be Normally distributed across groups.

Value

the fitted model

SWotherspoon/MVNSeq documentation built on June 10, 2025, 6:15 a.m.