est_mc_cov {LMest} | R Documentation |
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The function is no longer maintained.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)