Estimate mixture latent variable model

Estimate mixture latent variable model

mvnmix(
  data,
  k = 2,
  theta,
  steps = 500,
  tol = 1e-16,
  lambda = 0,
  mu = NULL,
  silent = TRUE,
  extra = FALSE,
  n.start = 1,
  init = "kmpp",
  ...
)

Arguments

data

data.frame

k

Number of mixture components

theta

Optional starting values

steps

Maximum number of iterations

tol

Convergence tolerance of EM algorithm

lambda

Regularisation parameter. Added to diagonal of covariance matrix (to avoid singularities)

mu

Initial centres (if unspecified random centres will be chosen)

silent

Turn on/off output messages

extra

Extra debug information

n.start

Number of restarts

init

Function to choose initial centres

...

Additional arguments parsed to lower-level functions

Value

A mixture object

Details

Estimate parameters in a mixture of latent variable models via the EM algorithm.

See also

mixture

Author

Klaus K. Holst

Examples

data(faithful)
set.seed(1)
M1 <- mvnmix(faithful[,"waiting",drop=FALSE],k=2)
M2 <- mvnmix(faithful,k=2)
if (interactive()) {
    par(mfrow=c(2,1))
    plot(M1,col=c("orange","blue"),ylim=c(0,0.05))
    plot(M2,col=c("orange","blue"))
}