fit_mixture function

Fit a generic mixture using an ECME-Algorithm

fit_mixture(
  dist,
  obs,
  start,
  min_iter = 0L,
  max_iter = 100L,
  skip_first_e = FALSE,
  tolerance = 1e-05,
  trace = FALSE,
  ...
)

Arguments

• dist: A MixtureDistribution specifying the structure of the mixture. Free parameters are to be optimised. The dominating measure for likelihoods must be constant, so for example dist_dirac() may not have its point

  parameter free.

• obs: Set of observations as produced by trunc_obs() or convertible via as_trunc_obs().

• start: Initial values of all placeholder parameters. If missing, starting values are obtained from fit_dist_start().

• min_iter: Minimum number of EM-Iterations

• max_iter: Maximum number of EM-Iterations (weight updates)

• skip_first_e: Skip the first E-Step (update Probability weights)? This can help if the initial values cause a mixture component to vanish in the first E-Step before the starting values can be improved.

• tolerance: Numerical tolerance.

• trace: Include tracing information in output? If TRUE, additional tracing information will be added to the result list.

• ...: Passed to fit_dist_start() if start is missing.

Returns

A list with elements

• params the fitted parameters in the same structure as init.
• params_hist (if trace is TRUE) the history of parameters (after each e- and m- step)
• iter the number of outer EM-iterations
• logLik the final log-likelihood

Examples

dist <- dist_mixture(
  list(
    dist_dirac(0.0),
    dist_exponential()
  )
)

params <- list(
  probs = list(0.1, 0.9),
  dists = list(
    list(),
    list(rate = 1.0)
  )
)

x <- dist$sample(100L, with_params = params)

fit_mixture(dist, x)

See Also

Other distribution fitting functions: fit_blended(), fit_dist(), fit_erlang_mixture()