Description Usage Arguments Value References Examples

This function chooses one of the three models.

1 |

`d` |
number of areas. |

`t` |
number of time periods. |

`pp` |
vector with the number of the auxiliary variables per category. |

`Xk` |
list of matrices with the auxiliary variables per category obtained from |

`X` |
list of matrices with the auxiliary variables obtained from |

`Z` |
design matrix of random effects obtained from |

`initial` |
output of the function |

`y` |
matrix with the response variable obtained from |

`M` |
vector with the area sample sizes. |

`MM` |
vector with the population sample sizes. |

`mod` |
a number specifying the type of models: 1=multinomial mixed model with one independent random effect in each category of the response variable (Model 1), 2=multinomial mixed model with two independent random effects in each category of the response variable: one domain random effect and another independent time and domain random effect (Model 2) and 3= multinomial model with two independent random effects in each category of the response variable: one domain random effect and another correlated time and domain random effect (Model 3). |

the output of the function `modelfit1`

, `modelfit2`

or `modelfit3`

.

Lopez-Vizcaino, ME, Lombardia, MJ and Morales, D (2013). Multinomial-based small area estimation of labour force indicators. Statistical Modelling, 13 ,153-178.

Lopez-Vizcaino, ME, Lombardia, MJ and Morales, D (2013). Small area estimation of labour force indicator under a multinomial mixed model with correlated time and area effects. Submitted for review.

1 2 3 4 5 6 7 | ```
k=3 #number of categories of the response variable
pp=c(1,1) #vector with the number of auxiliary variables in each category
data(simdata) #data
mod=1 #Model 1
datar=data.mme(simdata,k,pp,mod)
result=model(datar$d,datar$t,pp,datar$Xk,datar$X,datar$Z,datar$initial,datar$y[,1:(k-1)],
datar$n,datar$N, mod)
``` |

mme documentation built on May 30, 2017, 3:38 a.m.

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