LMlatent-class {LMest}

Class 'LMlatent'

Description

An S3 class object created by lmest for Latent Markov (LM) model with covariates in the latent model.

Value

lk

maximum log-likelihood

Be

estimated array of the parameters affecting the logit for the initial probabilities

Ga

estimated array of the parameters affecting the logit for the transition probabilities

Piv

estimate of initial probability matrix. The first state is used as reference category when param = "multilogit"

PI

estimate of transition probability matrices. State u is used as reference category when paramLatent = "multilogit"

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 of the EM algorithm

n

number of observations in the data

TT

number of time occasions

paramLatent

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)

sePsi

standard errors for the conditional response matrix

seBe

standard errors for Be

seGa

standard errors for Ga

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 posterior distribution 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