Description Value Author(s) See Also

An S3 class object created by `lmest`

for Latent Markov (LM) model with covariates in the measurement model.

`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 |

`PRED0` |
prediction of latent state |

`PRED1` |
prediction of the overall latent effect |

`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 |

`call` |
command used to call the function |

`data` |
data frame given in input |

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

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