LMbasic-class: Class "LMbasic"

In LMest: Generalized Latent Markov Models

Class 'LMbasic'

Description

An S3 class object created by lmest function for basic Latent Markov (LM) model.

Value

lk	maximum log-likelihood at convergence of the EM algorithm
piv	estimate of initial probability vector
Pi	estimate of transition probability matrices (k x k x TT)
Psi	estimate of conditional response probabilities (mb x k x r)
np	number of free parameters
k	optimal number of latent states
aic	value of the Akaike Information Criterion for model selection
bic	value of the Bayesian Information Criterion for model selection
lkv	log-likelihood trace at every step
n	number of observations in the data
TT	number of time occasions
modBasic	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.
sepiv	standard errors for the initial probabilities
sePi	standard errors for the transition probabilities
sePsi	standard errors for the conditional response probabilities
Lk	vector containing the values of the log-likelihood of the LM model with each k (latent states)
Bic	vector containing the values of the BIC for each k
Aic	vector containing the values of the AIC for each k
V	array containing the estimated posterior probabilities of the latent states for each response configuration and time occasion
Ul	matrix containing the predicted sequence of latent states by the local decoding method
S	array containing the available response configurations
yv	vector of frequencies of the available configurations
Pmarg	matrix containing the marginal distribution of the latent states
call	command used to call the function
data	data.frame given in input

Author(s)

Francesco Bartolucci, Silvia Pandolfi, Fulvia Pennoni, Alessio Farcomeni, Alessio Serafini

See Also

lmest

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