obpFH: Observed best predictor for Fay-Herriot model.

In rohosen/OBPSAE: Observed best predictors for small area estimation

Description

This function computes the 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). In the process of of computing OBP it also calculates the BPE of the regression coefficients of the fixed effect.

Usage

obpFH(formula, data, errorvar, randvar = NULL, maxiter = 100,
  precision = 1e-04)

Arguments

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	optional data frame containing the variable names in formula.
errorvar	vector containing the variances of the random errors for all small areas.
randvar	variance of the random effect. If not supplied, it is estimated by BPE.
maxiter	maximum number of iterations used in estimating randvar.
precision	covergence tolerance limit for estimating randvar.

Details

If randvar is not provided, it is first estimated by its BPE.

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.

Value

The function will return a list with the following objects

theta.OBP	OBP of the small area mean.
A.BPE	BPE of variance of the random effect (if not specified by the user).
beta.BPE	BPE of the regression coefficients of the fixed effect.

References

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

rohosen/OBPSAE documentation built on May 17, 2019, 2:22 p.m.