search.model.LM: Search for the global maximum of the log-likelihood

In LMest: Generalized Latent Markov Models

Search for the global maximum of the log-likelihood

Description

Function that searches for the global maximum of the log-likelihood of different models given a vector of possible number of states to try for.

The function is no longer maintained. Please look at lmestSearch function.

Usage

search.model.LM(version = c("basic","latent","manifest","basic.cont", "latent.cont"),
                kv, ..., nrep = 2, tol1 = 10^-5, tol2 = 10^-10,out_se = FALSE)

Arguments

version	model to be estimated ("basic" = basic LM model (est_lm_basic function); "latent" = LM model with covariates in the distribution of the latent process (est_lm_cov_latent function); "manifest" = LM model with covariates in the measurement model (est_lm_cov_maifest function),"basic.cont" = basic LM model for continuous outcomes (est_lm_basic_cont function); "latent.cont" = LM model for continuous outcomes with covariates in the distribution of the latent process (est_lm_cov_latent_cont function))
kv	vector of possible number of latent states
...	additional arguments to be passed based on the model to be estimated (see details)
nrep	number of repetitions of each random initialization
tol1	tolerance level for checking convergence of the algorithm in the random initializations
tol2	tolerance level for checking convergence of the algorithm in the last deterministic initialization
out_se	TRUE for computing information matrix and standard errors

Details

The function combines deterministic and random initializations strategy to reach the global maximum of the model log-likelihood. It uses one deterministic initialization (start=0) and a number of random initializations (start=1) proportional to the number of latent states. The tolerance level is set equal to 10^-5. Starting from the best solution obtained in this way, a final run is performed (start=2) with a default tolerance level equal to 10^-10.

Arguments in ... depend on the model to be estimated. They match the arguments to be passed to functions est_lm_basic, est_lm_cov_latent, est_lm_cov_manifest, est_lm_basic_cont, or est_lm_cov_latent_cont.

Value

out.single	output of each single model (as from est_lm_basic, est_lm_cov_latent or est_lm_cov_manifest) for each k in kv
aicv	value of AIC index for each k in kv
bicv	value of BIC index for each k in kv
lkv	value of log-likelihood for each k in kv

Author(s)

Francesco Bartolucci, Silvia Pandolfi, University of Perugia (IT), http://www.stat.unipg.it/bartolucci

Examples

## Not run: 

# example for est_lm_basic
data(data_drug)
data_drug <- as.matrix(data_drug)
S <- data_drug[,1:5]-1
yv <- data_drug[,6]
n <- sum(yv)
# Search Basic LM model

res <- search.model.LM("basic", kv = 1:4, S, yv, mod = 1)
summary(res)


## End(Not run)

LMest documentation built on Sept. 16, 2024, 1:07 a.m.