est_lm_basic: Estimate basic LM model

In LMest: Generalized Latent Markov Models

Estimate basic LM model

Description

Main function for estimating the basic LM model.

The function is no longer maintained.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

Usage

est_lm_basic(S, yv, k, start = 0, mod = 0, tol = 10^-8, maxit = 1000,
	                  out_se = FALSE, piv = NULL, Pi = NULL, Psi = NULL)

Arguments

S	array of available configurations (n x TT x r) with categories starting from 0 (use NA for missing responses)
yv	vector of frequencies of the available configurations
k	number of latent states
start	type of starting values (0 = deterministic, 1 = random, 2 = initial values in input)
mod	model on the transition probabilities (0 for time-heter., 1 for time-homog., from 2 to (TT-1) partial homog. of that order)
tol	tolerance level for convergence
maxit	maximum number of iterations of the algorithm
out_se	to compute the information matrix and standard errors
piv	initial value of the initial probability vector (if start=2)
Pi	initial value of the transition probability matrices (k x k x TT) (if start=2)
Psi	initial value of the conditional response probabilities (mb x k x r) (if start=2)

Value

lk	maximum log-likelihood
piv	estimate of initial probability vector
Pi	estimate of transition probability matrices
Psi	estimate of conditional response probabilities
np	number of free parameters
aic	value of AIC for model selection
bic	value of BIC for model selection
lkv	log-likelihood trace at every step
V	array containing the posterior distribution of the latent states for each response configuration and time occasion
sepiv	standard errors for the initial probabilities
sePi	standard errors for the transition probabilities
sePsi	standard errors for the conditional response probabilities
call	command used to call the function

Author(s)

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

References

Bartolucci, F., Farcomeni, A. and Pennoni, F. (2013) Latent Markov Models for Longitudinal Data, Chapman and Hall/CRC press.

Examples

## Not run: 
# Example of drug consumption data

# load data
data(data_drug)
data_drug <- as.matrix(data_drug)
S <- data_drug[,1:5]-1
yv <- data_drug[,6]

# fit of the Basic LM model
k <- 3
out <- est_lm_basic(S, yv, k, mod = 1)
summary(out)

# Example based on criminal data

# load criminal data
data(data_criminal_sim)
out <- long2wide(data_criminal_sim, "id" , "time" , "sex",
c("y1","y2","y3","y4","y5","y6","y7","y8","y9","y10"),aggr = T, full = 999)
XX <- out$XX
YY <- out$YY
freq <- out$freq

# fit basic LM model with increasing number of states to select the most suitable
Res0 <- vector("list", 7)
for(k in 1:7){
    Res0[[k]] <- est_lm_basic(YY, freq, k, mod = 1, tol = 10^-4)
    save(list <- ls(), file = "example_criminal_temp.RData")
}
out1 <- Res0[[6]]

## End(Not run)

LMest documentation built on Aug. 27, 2023, 5:06 p.m.