# BLE_Reg: General BLE case In BayesSampling: Bayes Linear Estimators for Finite Population

## Description

Calculates the Bayes Linear Estimator for Regression models (general case)

## Usage

 `1` ```BLE_Reg(ys, xs, a, R, Vs, x_nots, V_nots) ```

## Arguments

 `ys` response variable of the sample `xs` explicative variable of the sample `a` vector of means from Beta `R` covariance matrix of Beta `Vs` covariance of sample errors `x_nots` values of X for the individuals not in the sample `V_nots` covariance matrix of the individuals not in the sample

## Value

A list containing the following components:

• `est.beta` - BLE of Beta

• `Vest.beta` - Variance associated with the above

• `est.mean` - BLE of each individual not in the sample

• `Vest.mean` - Covariance matrix associated with the above

• `est.tot` - BLE for the total

• `Vest.tot` - Variance associated with the above

## References

GonÃ§alves, K.C.M, Moura, F.A.S and Migon, H.S.(2014). Bayes Linear Estimation for Finite Population with emphasis on categorical data. Survey Methodology, 40, 15-28.

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10``` ```xs <- matrix(c(1,1,1,1,2,3,5,0),nrow=4,ncol=2) ys <- c(12,17,28,2) x_nots <- matrix(c(1,1,1,0,1,4),nrow=3,ncol=2) a <- c(1.5,6) R <- matrix(c(10,2,2,10),nrow=2,ncol=2) Vs <- diag(c(1,1,1,1)) V_nots <- diag(c(1,1,1)) Estimator <- BLE_Reg(ys, xs, a, R, Vs, x_nots, V_nots) Estimator ```

BayesSampling documentation built on May 2, 2021, 1:06 a.m.