LMbasic-class {LMest}

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