model_norm

model_norm

Usage

model_norm(
  input.matrix,
  k = 2,
  lambda = c(0.95, 0.05),
  mu = c(5, 25),
  sigma = c(1, 5),
  fallback.fun = function(x) quantile(x, c(0.2, 0.9)),
  max.restarts = 10,
  max.iter = 2000
)

Arguments

input.matrix

A matrix

k

The number of mixture components

lambda

The estimated proportions of the mixture models

mu

The estimated means of the mixture models

sigma

The estimated standard deviations of the mixture models

fallback.fun

In case the mixture algorithm fails, what is the fallback function to define the noise distribution?

max.restarts

How many times the EM maximization should re-initiate if variances go to zero?

max.iter

How many iterations should the algorithm take to converge before giving up?

Value

Info about the fitted GMM. See model_norm_region for more details.