Description Usage Arguments Value Author(s) References Examples

Main function for estimating the mixed LM model with discrete random effect in the latent model.

**The function is no longer maintained. Please look at** `lmestMixed`

**function**

1 2 |

`S` |
array of available response configurations (n x TT x r) with categories starting from 0 |

`yv` |
vector of frequencies of the available configurations |

`k1` |
number of latent classes |

`k2` |
number of latent states |

`start` |
type of starting values (0 = deterministic, 1 = random) |

`tol` |
tolerance level for convergence |

`maxit` |
maximum number of iterations of the algorithm |

`out_se` |
to compute standard errors |

`la ` |
estimate of the mass probability vector (distribution of the random effects) |

`Piv ` |
estimate of initial probabilities |

`Pi ` |
estimate of transition probability matrices |

`Psi ` |
estimate of conditional response probabilities |

`lk ` |
maximum log-likelihood |

`W ` |
posterior probabilities of the random effect |

`np ` |
number of free parameters |

`bic ` |
value of BIC for model selection |

`call` |
command used to call the function |

Francesco Bartolucci, Silvia Pandolfi - University of Perugia (IT)

Bartolucci, F., Farcomeni, A. and Pennoni, F. (2013) *Latent Markov Models for Longitudinal Data*,
Chapman and Hall/CRC press.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 | ```
## Not run:
# Example based of criminal data
# load data
data(data_criminal_sim)
out <- long2wide(data_criminal_sim, "id", "time", "sex",
c("y1","y2","y3","y4","y5","y6","y7","y8","y9","y10"), aggr = T, full = 999)
XX <- out$XX
YY <- out$YY
freq <- out$freq
n1 <- sum(freq[XX[,1] == 1])
n2 <- sum(freq[XX[,1] == 2])
n <- sum(freq)
# fit mixed LM model only for females
YY <- YY[XX[,1] == 2,,]
freq <- freq[XX[,1] == 2]
k1 <- 2
k2 <- 2
res <- est_lm_mixed(YY, freq, k1, k2, tol = 10^-8)
summary(res)
## End(Not run)
``` |

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