BLE_SSRS {BayesSampling} | R Documentation |
Creates the Bayes Linear Estimator for the Stratified Simple Random Sampling design (without replacement)
BLE_SSRS(ys, h, N, m = NULL, v = NULL, sigma = NULL)
ys |
vector of sample observations or sample mean for each strata ( |
h |
vector with number of observations in each strata. |
N |
vector with the total size of each strata. |
m |
vector with the prior mean of each strata. If |
v |
vector with the prior variance of an element from each strata (bigger than |
sigma |
vector with the prior estimate of variability (standard deviation) within each strata of the population. If |
A list containing the following components:
est.beta
- BLE of Beta (BLE for the individuals in each strata)
Vest.beta
- Variance associated with the above
est.mean
- BLE for 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.
ys <- c(2,-1,1.5, 6,10, 8,8) h <- c(3,2,2) N <- c(5,5,3) m <- c(0,9,8) v <- c(3,8,1) sigma <- c(1,2,0.5) Estimator <- BLE_SSRS(ys, h, N, m, v, sigma) Estimator # Same example but informing sample means instead of sample observations y1 <- mean(c(2,-1,1.5)) y2 <- mean(c(6,10)) y3 <- mean(c(8,8)) ys <- c(y1, y2, y3) h <- c(3,2,2) N <- c(5,5,3) m <- c(0,9,8) v <- c(3,8,1) sigma <- c(1,2,0.5) Estimator <- BLE_SSRS(ys, h, N, m, v, sigma) Estimator