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Lognormal mixture model - Expectation-Maximization Algorithm

survival_ln_mixture_em.Rd

survival_ln_mixture_em() fits an EM algorithm, as described in LOBO, Viviana GR; FONSECA, Thaís CO; ALVES, Mariane B. Lapse risk modeling in insurance: a Bayesian mixture approach. Annals of Actuarial Science, v. 18, n. 1, p. 126-151, 2024, for modelling mixtures of lognormal distributions applied to survival data.

Usage

survival_ln_mixture_em(
  formula,
  data,
  intercept = TRUE,
  iter = 50,
  mixture_components = 2,
  starting_seed = sample(1:2^28, 1),
  number_em_search = 200,
  iteration_em_search = 1,
  show_progress = FALSE,
  ...
)

# Default S3 method
survival_ln_mixture_em(formula, ...)

# S3 method for class 'formula'
survival_ln_mixture_em(formula, data, intercept = TRUE, ...)

Arguments

formula

A formula specifying the outcome terms on the left-hand side, and the predictor terms on the right-hand side. The outcome must be a survival::Surv object.

data

A data frame containing both the predictors and the outcome.

intercept

A logical. Should an intercept be included in the processed data?

iter

A positive integer specifying the number of iterations for the EM algorithm.

mixture_components

number of mixture componentes >= 2.

starting_seed

Starting seed for the algorithm. If not specified by the user, uses a random integer between 1 and 2^28 This way we ensure, when the user sets a seed in R, that this is passed into the C++ code.

Number of different EM's to search for maximum likelihoods. Recommended to leave, at least, at 100.

Number of iterations for each of the EM's used to find the maximum likelihoods. Recommended to leave at small values, such as from 1 to 5.

show_progress

A logical. Should the progress of the EM algorithm be shown?

...

Not currently used, but required for extensibility.

Value

An object of class survival_ln_mixture_em containing the following elements:

  • em_iterations: A data frame containing the EM iterations.

  • nobs: The number of observations.

  • predictors_name: The names of the predictors.

  • logLik: The log-likelihood of the model.

  • mixture_groups: The number of mixture groups.

  • blueprint: The blueprint used to process the formula