est_mc_cov: Estimate Markov chain (MC) model with covariates

In LMest: Generalized Latent Markov Models

Estimate Markov chain (MC) model with covariates

Description

Main function for estimating the MC model with covariates.

The function is no longer maintained.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

Usage

est_mc_cov(S, X1 = NULL, X2 = NULL, yv = rep(1,nrow(S)), start = 0, tol = 10^-8,
	   maxit = 1000, out_se = FALSE, output = FALSE, fort = TRUE)

Arguments

S	matrix of available configurations of the response variable (n x TT) with categories starting from 0
X1	matrix of covariates affecting the initial probabilities (n x nc1)
X2	array of covariates affecting the transition probabilities (n x TT-1 x nc2)
yv	vector of frequencies of the available configurations
start	type of starting values (0 = deterministic, 1 = random)
tol	tolerance level for checking convergence of the algorithm
maxit	maximum number of iterations of the algorithm
out_se	to compute the information matrix and standard errors
output	to return additional output (PI,Piv)
fort	to use fortran routine when possible (FALSE for not use fortran)

Value

lk	maximum log-likelihood
Be	estimated array of the parameters affecting the logit for the initial probabilities
Ga	estimated array of the parameters affecting the logit for the transition probabilities
np	number of free parameters
aic	value of AIC for model selection
bic	value of BIC for model selection
seBe	standard errors for Be
seGa	standard errors for Ga
Piv	estimate of initial probability matrix
PI	estimate of transition probability matrices
call	command used to call the function

Author(s)

Francesco Bartolucci, Silvia Pandolfi, University of Perugia, 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 based on criminal data

# load criminal data
data(data_criminal_sim)

#We consider the response variable referring of crime of type 5

out <- long2wide(data_criminal_sim, "id", "time", "sex",
"y5", aggr = T, full = 999)
XX <- out$XX-1
YY <- out$YY
freq <- out$freq
TT <- 6

X1 <- as.matrix(XX[,1])
X2 <- as.matrix(XX[,2:TT])
# estimate the model
res <- est_mc_cov(S = YY, yv = freq, X1 = X1, X2 = X2, output = TRUE)
summary(res)

# Initial probability for female
Piv0 <- round(colMeans(res$Piv[X1 == 0,]), 4)

# Initial probability for male
Piv1 <- round(colMeans(res$Piv[X1 == 1,]), 4)


## End(Not run)

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