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

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

Description

This function generates a mixture model combining left-truncated lognormal, gamma, and weibull distributions

Usage

ltmm(
  x,
  G,
  distributions,
  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
)

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?

Value

An ltmm model object, with the following properties:

x

Copy of the input data

distributions

The selected distributions

trunc

The left truncation value, if specified

fitted_pdf

The probability density function of the fitted model

fitted_cfd

The cumulative density function of the fitted model

VaR

The value-at-risk of the fitted model (function with p taken as onl yargument)

ES

The expected shortfall of the fitted model (function with p taken as onl yargument)

G

The number of components in the model

Pi

The estimated probabilites of component membership

Pars

The estimated model parameters

ll

The log-likelihood of the fitted model

bic

The BIC of the fitted model

aic

The AIC of the fitted model

id

The MAP component membership for each observation

iter

The number of iterations until convergence for the EM algorithm

npars

The total number of model parameters for the fitted model

ll.history

The value of log-likelihood at each iteration of the EM algorithm

Examples

x <- secura$Loss

fit <- ltmm(x, G = 2, distributions = c('gamma', 'gamma', 'weibull'), trunc = 1.2e6)

summary(fit)
plot(fit)

ltmix documentation built on July 5, 2021, 9:06 a.m.