BLE_SSRS {BayesSampling}  R Documentation 
Stratified Simple Random Sample BLE
Description
Creates the Bayes Linear Estimator for the Stratified Simple Random Sampling design (without replacement)
Usage
BLE_SSRS(ys, h, N, m = NULL, v = NULL, sigma = NULL)
Arguments
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 
Value
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
Source
https://www150.statcan.gc.ca/n1/en/catalogue/12001X201400111886
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, 1528.
Examples
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