survival_ln_mixture_em: Lognormal mixture model - Expectation-Maximization Algorithm

In lnmixsurv: Bayesian Mixture Log-Normal Survival Model

Lognormal mixture model - Expectation-Maximization Algorithm

Description

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.

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

lnmixsurv documentation built on Sept. 11, 2024, 7:18 p.m.