Main function for estimating the LM model with covariates in the latent model.
The function is no longer maintained. Please look at lmest function.
est_lm_cov_latent(S, X1=NULL, X2=NULL, yv = rep(1,nrow(S)), k, start = 0, tol = 10^-8,
maxit = 1000, param = "multilogit", Psi, Be, Ga, fort = TRUE,
output = FALSE, out_se = FALSE, fixPsi = FALSE)
maximum log-likelihood
estimated array of the parameters affecting the logit for the initial probabilities
estimated array of the parameters affecting the logit for the transition probabilities
estimate of initial probability matrix
estimate of transition probability matrices
estimate of conditional response probabilities
number of free parameters
value of AIC for model selection
value of BIC for model selection
log-likelihood trace at every step
array containing the posterior distribution of the latent states for each response configuration and time occasion
matrix containing the predicted sequence of latent states by the local decoding method
standard errors for the conditional response matrix
standard errors for Be
standard errors for Ga
command used to call the function
array of available configurations (n x TT x r) with categories starting from 0 (use NA for missing responses)
matrix of covariates affecting the initial probabilities (n x nc1)
array of covariates affecting the transition probabilities (n x TT-1 x nc2)
vector of frequencies of the available configurations
number of latent states
type of starting values (0 = deterministic, 1 = random, 2 = initial values in input)
tolerance level for checking convergence of the algorithm
maximum number of iterations of the algorithm
type of parametrization for the transition probabilities ("multilogit" = standard multinomial logit for every row of the transition matrix, "difflogit" = multinomial logit based on the difference between two sets of parameters)
intial value of the array of the conditional response probabilities (mb x k x r)
intial value of the parameters affecting the logit for the initial probabilities (if start=2)
intial value of the parametes affecting the logit for the transition probabilities (if start=2)
to use fortran routine when possible (FALSE for not use fortran)
to return additional output (V,PI,Piv,Ul)
to compute the information matrix and standard errors
TRUE if Psi is given in input and is not updated anymore
Francesco Bartolucci, Silvia Pandolfi, University of Perugia, 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.