ltmmCombo: Fit a Left-truncated mixture model (LTMM)

In ltmix: Left-Truncated Mixtures of Gamma, Weibull, and Lognormal Distributions

Fit a Left-truncated mixture model (LTMM)

Description

This function fits a family of finite mixture models using every combination of the left-truncated lognormal, gamma, and weibull distributions.

Usage

ltmmCombo(
  x,
  G,
  distributions = c("lognormal", "gamma", "weibull"),
  trunc = NULL,
  EM_init_method = "emEM",
  EM_starts = 5,
  init_pars = NULL,
  init_pi = NULL,
  init_classes = NULL,
  one_group_reps = 50,
  eps = 1e-06,
  max.it = 1000,
  verbose = FALSE,
  parallel = FALSE,
  cores = NULL,
  save_each_fit = FALSE
)

Arguments

x	data vector
G	number of components
distributions	densities to combine
trunc	left truncation point (optional)
EM_init_method	initialization method for EM algorithm
EM_starts	number of random starts for initialization of EM algorithm. (only for G > 1)
init_pars	initial parameter values (list of length G)
init_pi	manually specified initial component proportions (for init_method=specified)
init_classes	manually specified initial classes. will overwrite init_pars and init_pi
one_group_reps	number of random starts for each numerical optimization in 1-component model
eps	stopping tolerance for EM algoithm
max.it	maximum number of iterations of EM algorithm
verbose	print information as fitting progresses?
parallel	fit models in parallel?
cores	number of processes used for parallel computation. if NULL detect.cores() used
save_each_fit	save each model as it is produced, in a time-stamped directory (safer)

Value

An ltmmCombo model object, with the following properties:

x

Copy of the input data

distributions

The selected distributions

combos

List of all combinations of distributions considered

all.fits

List of all ltmm fit objects

all.bic

Vector of BIC values for each model

best.bic.fit

The best ltmm fit by BIC

best.bic

The best BIC value of all fits

best.bic.combo

The combination of distributions used for the best fit by BIC

all.aic

Vector of AIC value for each model

best.aic.fit

The best ltmm fit by AIC

best.aic

The best AIC value of all fits

best.aic.combo

The combination of distributions used for the best fit by AIC

all.ll

Vector of log-likelihood value for each model

summary_table

Table summarizing the AIC, BIC, LL, and risk measures for each fitted model

References

Blostein, Martin & Miljkovic, Tatjana. (2019). On modeling left-truncated loss data using mixtures of distributions. Insurance Mathematics and Economics. 85. 35-46. 10.1016/j.insmatheco.2018.12.001.

Examples

x <- secura$Loss

fits_GL <- ltmmCombo(x, G = 2, distributions = c('gamma', 'lognormal'), trunc = 1.2e6)
summary(fits_GL)

ltmix documentation built on June 22, 2024, 7:02 p.m.