Lognormal mixture model - Expectation-Maximization Algorithm — survival_ln_mixture

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