est_mc_basic | R Documentation |
Main function for estimating the basic MC model.
The function is no longer maintained. Please look at lmestMc
function.
est_mc_basic(S, yv, mod = 0, tol = 10^-8, maxit = 1000, out_se = FALSE)
S |
matrix (n x TT) of available configurations of the response variable with categories starting from 0 |
yv |
vector of frequencies of the available configurations |
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 |
lk |
maximum log-likelihood |
piv |
estimate of initial probability vector |
Pi |
estimate of transition probability matrices |
np |
number of free parameters |
aic |
value of AIC for model selection |
bic |
value of BIC for model selection |
Fy |
estimated marginal distribution of the response variable for each time occasion |
sepiv |
standard errors for the initial probabilities |
sePi |
standard errors for the transition probabilities |
call |
command used to call the function |
Francesco Bartolucci, Silvia Pandolfi, University of Perugia (IT), http://www.stat.unipg.it/bartolucci
Bartolucci, F., Farcomeni, A. and Pennoni, F. (2013) Latent Markov Models for Longitudinal Data, Chapman and Hall/CRC press.
# 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 MC model
out <- est_mc_basic(S, yv, mod = 1, out_se = TRUE)
summary(out)
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