LMmanifest-class: Class "LMmanifest"

In LMest: Generalized Latent Markov Models

Class 'LMmanifest'

Description

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

Value

mu	vector of cut-points
al	support points for the latent states
be	estimate of the vector of regression parameters
si	sigma of the AR(1) process (mod = "FM")
rho	parameter vector for AR(1) process (mod = "FM")
la	vector of initial probabilities
PI	transition matrix
lk	maximum log-likelihood
np	number of parameters
k	optimal number of latent states
aic	value of the Akaike Information Criterion
bic	value of Bayesian Information Criterion
n	number of observations in the data
TT	number of time occasions
modManifest	for LM model with covariates on the manifest model: "LM" = Latent Markov with stationary transition, "FM" = finite mixture model where a mixture of AR(1) processes is estimated with common variance and specific correlation coefficients
sebe	standard errors for the regression parameters be
selrho	standard errors for logit type transformation of rho
J1	information matrix
V	array containing the posterior distribution of the latent states for each units and time occasion
PRED1	prediction of the overall latent effect
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
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
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.