Description Usage Arguments Value Author(s) References Examples

Main function for estimating the basic LM model.

**The function is no longer maintained.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

**function**.

1 2 |

`S` |
array of available configurations (n x TT x r) with categories starting from 0 (use NA for missing responses) |

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

`k` |
number of latent states |

`start` |
type of starting values (0 = deterministic, 1 = random, 2 = initial values in input) |

`mod` |
model on the transition probabilities (0 for time-heter., 1 for time-homog., from 2 to (TT-1) partial homog. of that order) |

`tol` |
tolerance level for convergence |

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

`out_se` |
to compute the information matrix and standard errors |

`piv ` |
initial value of the initial probability vector (if start=2) |

`Pi ` |
initial value of the transition probability matrices (k x k x TT) (if start=2) |

`Psi ` |
initial value of the conditional response probabilities (mb x k x r) (if start=2) |

`lk ` |
maximum log-likelihood |

`piv ` |
estimate of initial probability vector |

`Pi ` |
estimate of transition probability matrices |

`Psi ` |
estimate of conditional response probabilities |

`np ` |
number of free parameters |

`aic ` |
value of AIC for model selection |

`bic ` |
value of BIC for model selection |

`lkv ` |
log-likelihood trace at every step |

`V ` |
array containing the posterior distribution of the latent states for each response configuration and time occasion |

`sepiv` |
standard errors for the initial probabilities |

`sePi` |
standard errors for the transition probabilities |

`sePsi` |
standard errors for the conditional response probabilities |

`call` |
command used to call the function |

Francesco Bartolucci, Silvia Pandolfi, University of Perugia (IT), http://www.stat.unipg.it/bartolucci

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 25 26 27 28 29 30 31 32 33 | ```
## Not run:
# Example of drug consumption data
# load data
data(data_drug)
data_drug <- as.matrix(data_drug)
S <- data_drug[,1:5]-1
yv <- data_drug[,6]
# fit of the Basic LM model
k <- 3
out <- est_lm_basic(S, yv, k, mod = 1)
summary(out)
# Example based on criminal data
# load criminal 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
# fit basic LM model with increasing number of states to select the most suitable
Res0 <- vector("list", 7)
for(k in 1:7){
Res0[[k]] <- est_lm_basic(YY, freq, k, mod = 1, tol = 10^-4)
save(list <- ls(), file = "example_criminal_temp.RData")
}
out1 <- Res0[[6]]
## End(Not run)
``` |

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