mvnmix: Estimate mixture latent variable model

In kkholst/lava: Latent Variable Models

Estimate mixture latent variable model

Description

Estimate mixture latent variable model

Usage

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

Details

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

Value

A mixture object

Author(s)

Klaus K. Holst

See Also

mixture

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"))
}

kkholst/lava documentation built on March 4, 2023, 10:35 p.m.