Lognormal mixture model - Gibbs sampler
survival_ln_mixture.Rdsurvival_ln_mixture() fits a Bayesian lognormal mixture model with Gibbs sampling (optional EM algorithm to find local maximum at the likelihood function), 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.
Usage
survival_ln_mixture(
formula,
data,
intercept = TRUE,
iter = 1000,
warmup = floor(iter/10),
thin = 1,
chains = 1,
cores = 1,
mixture_components = 2,
show_progress = FALSE,
em_iter = 0,
starting_seed = sample(1:2^28, 1),
use_W = FALSE,
number_em_search = 200,
iteration_em_search = 1,
fast_groups = TRUE,
...
)
# Default S3 method
survival_ln_mixture(formula, ...)
# S3 method for class 'formula'
survival_ln_mixture(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 each chain (including warmup).
- warmup
A positive integer specifying the number of warmup (aka burnin) iterations per chain. The number of warmup iterations should be smaller than iter.
- thin
A positive integer specifying the period for saving samples.
- chains
A positive integer specifying the number of Markov chains.
- cores
A positive integer specifying the maximum number of cores to run the chains. Setting this to a value bigger than 1 will automatically trigger the parallel mode
- mixture_components
number of mixture componentes >= 2.
- show_progress
Indicates if the code shows the progress of the EM algorithm and the Gibbs Sampler.
- em_iter
A positive integer specifying the number of iterations for the EM algorithm. The EM algorithm is performed before the Gibbs sampler to find better initial values for the chains. On simulations, values lower than 200 seems to work nice.
- starting_seed
Starting seed for the sampler. 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.
- use_W
Specifies is the W (groups weight's matrix for each observation) should be used from EM. It holds W constant through the code, resulting in a faster Bayesian Inference (close to what Empirical Bayes would do). It may helps generating credible intervals for the survival and hazard curves, using the information from the previous EM iteration. Make sure the EM have converged before setting this parameter to true. In doubt, leave this as FALSE, the default.
- number_em_search
Number of different EM's to search for maximum likelihoods. Recommended to leave, at least, at 100. This value can be set to 0 to disable the search for maximum likelihood initial values.
- 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.
- fast_groups
Use fast computation of groups allocations probabilities, defaults to TRUE. Setting it to FALSE can increase the computation time (a lot) but it's worth trying if the chains are not converging.
- ...
Not currently used, but required for extensibility.
Value
A survival_ln_mixture object, which is a list with the following componentes:
- posterior
A posterior::draws_matrix with the posterior of the parameters of the model.
- nobs
A integer holding the number of observations used to generate the fit.
- blueprint
The blueprint component of the output of hardhat::mold