BLE_Reg {BayesSampling} | R Documentation |
Calculates the Bayes Linear Estimator for Regression models (general case)
BLE_Reg(ys, xs, a, R, Vs, x_nots, V_nots)
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 |
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
https://www150.statcan.gc.ca/n1/en/catalogue/12-001-X201400111886
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.
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