Description Usage Arguments Details Value References

View source: R/obpFHbenchmark.R

This function computes the Augmented observed best predictor (OBP) for Fay-Herriot model. The variance of the random error can be specified by the user. Otherwise the function will calculate its Best Predictive Estimator (BPE) under the augmented model. In the process of of computing augmented OBP it also calculates the BPE of the regression coefficients of the fixed effect.

1 2 | ```
obpFH_augmented(formula, data, errorvar, weight, randvar = NULL,
maxiter = 100, precision = 1e-04)
``` |

`formula` |
an object of class formula (or one that can be coerced to that class): a symbolic description of the model to be fitted. The variables included in formula must have a length equal to the number of small areas. More about the model specification are given under Details. |

`data` |
data frame containing the variable names in formula and errorvar. |

`errorvar` |
vector containing the variances of the random errors for all small areas. |

`weight` |
vector containing the sampling weights of small areas. If sum of the weights is not 1, the weights are normalized. |

`randvar` |
variance of the random effect. If not supplied, BPE is estimated. |

`maxiter` |
maximum number of iterations used in estimating randvar. |

`precision` |
covergence tolerance limit for estimating randvar. |

The variance of the random effect can be specified by the user. Otherwise the function will calculate its Best Predictive Estimator (BPE) under the augmented model. In the process of of computing Adjusted OBP it also calculates the BPE of the regression coefficients of the fixed effect.

`formula`

is specified in the form `response ~ predictors`

where the predictors are separated by `+`

. `formula`

has an implied intercept term. To remove the intercept term, use either `y ~ x - 1`

or `y ~ 0 + x`

.

The function will return a list with all the following objects by default.

`obpAugmented` |
a vector of augmented OBP values. |

`A.BPE.aug` |
BPE of variance component of random effects under the augmented model (if not provided by the user). |

`beta.BPE.aug` |
BPE of fixed effects regression coefficients under the augmented model. |

Bandyopadhyay R, Jiang J (2017) "Benchmarking the Observed Best Predictor"

Jiang J, Nguyen T, and Rao J. S. (2011), "Best Predictive Small Area Estimation", Journal of the American Statistical Association.

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