LMbasic-class | R Documentation |
'LMbasic'
An S3 class object created by lmest
function for basic Latent Markov (LM) model.
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 |
sample size (sum of the weights when weights are provided) |
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 |
Bic |
vector containing the values of the BIC for each |
Aic |
vector containing the values of the AIC for each |
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 |
ns |
number of distinct response configurations |
call |
command used to call the function |
data |
data.frame given in input |
Francesco Bartolucci, Silvia Pandolfi, Fulvia Pennoni, Alessio Farcomeni, Alessio Serafini
lmest
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