'LMbasic'An S3 class object created by lmest function for basic Latent Markov (LM) model.
maximum log-likelihood at convergence of the EM algorithm
estimate of initial probability vector
estimate of transition probability matrices (k x k x TT)
estimate of conditional response probabilities (mb x k x r)
number of free parameters
optimal number of latent states
value of the Akaike Information Criterion for model selection
value of the Bayesian Information Criterion for model selection
log-likelihood trace at every step
number of observations in the data
number of time occasions
model on the transition probabilities: default 0 for time-heterogeneous transition matrices, 1 for time-homogeneous transition matrices, 2 for partial time homogeneity based on two transition matrices one from 2 to (TT-1) and the other for TT.
standard errors for the initial probabilities
standard errors for the transition probabilities
standard errors for the conditional response probabilities
vector containing the values of the log-likelihood of the LM model with each k (latent states)
vector containing the values of the BIC for each k
vector containing the values of the AIC for each k
array containing the estimated posterior probabilities of the latent states for each response configuration and time occasion
matrix containing the predicted sequence of latent states by the local decoding method
array containing the available response configurations
vector of frequencies of the available configurations
matrix containing the marginal distribution of the latent states
command used to call the function
data.frame given in input
Francesco Bartolucci, Silvia Pandolfi, Fulvia Pennoni, Alessio Farcomeni, Alessio Serafini
lmest