# The function eblup.saery calculate the eblup and mse for a model.

### Description

The function `eblup.saery`

calculate the eblup and mse for a model. Is recomended that the model was previusly checked by `fit.saery`

### Usage

1 2 3 4 5 6 | ```
eblup.saery(X, ydi, D, md, sigma2edi,
model = c("INDEP", "AR1", "MA1"),
plot = FALSE, type = "I", B = 100)
eblup.saery.AR1(X, ydi, D, md, sigma2edi, plot, type, B)
eblup.saery.MA1(X, ydi, D, md, sigma2edi, plot, type, B)
eblup.saery.indep(X, ydi, D, md, sigma2edi, plot)
``` |

### Arguments

`X` |
a numeric vector or data frame containing the aggregated (population) values of p auxiliary variables. A ones columns must be agregated to calculate the intercept parameter |

`ydi` |
a numeric vector with the direct estimator of the indicator of interest for area (domain) |

`D` |
a numeric vector with the number of areas (domain) of the data |

`md` |
a numeric vector with the number of periods (subdomains) for each area of the data |

`sigma2edi` |
a numeric vector with the known variance of the error term |

`model` |
Three diferents types of model must be fit. For an indepent model |

`plot` |
logical specifying if a set of plot be returned. |

`type` |
three types of mse can be calculated for |

`B` |
the number of bootstrap samples to be generated and fitted for types |

### Value

A data frame with the eblups and its mse be returned. A set of plots be displayed if `plot = TRUE`

### Author(s)

Maria Dolores Esteban Lefler, Domingo Morales Gonzalez, Agustin Perez Martin

### References

Rao, J.N.K., Yu, M., 1994. Small area estimation by combining time series and cross sectional data. Canadian Journal of Statistics 22, 511-528.

Esteban, M.D., Morales, D., Perez, A., Santamaria, L., 2012. Small area estimation of poverty proportions under area-level time models. Computational Statistics and Data Analysis, 56 (10), pp. 2840-2855.

### See Also

`fit.saery`

### Examples

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | ```
sigma2edi <- datos[,6]
X <- as.matrix(datos[,5])
ydi <- datos[,3]
D <- length(unique(datos[,1]))
md <- rep(length(unique(datos[,2])), D)
#For computational reasons B is too low. We recomend to increase up to 100
eblup.output.ar1 <- eblup.saery(X, ydi, D, md, sigma2edi, model = "a", plot = TRUE, B = 2)
eblup.output.ar1
#For computational reasons B is too low. We recomend to increase up to 100
eblup.output.ma1 <- eblup.saery(X, ydi, D, md, sigma2edi,
model = "ma", plot = FALSE, type = "II", B = 2)
eblup.output.ma1
eblup.output.indep <- eblup.saery(X, ydi, D, md, sigma2edi,
model = "i", plot = TRUE)
eblup.output.indep
``` |