BayesSampling (version 1.1.0)

BLE_SSRS: 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 (sigma parameter will be required in this case).

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 NULL, sample mean for each strata will be used (non-informative prior).

v

vector with the prior variance of an element from each strata (bigger than sigma^2 for each strata). If NULL, it will tend to infinity (non-informative prior).

sigma

vector with the prior estimate of variability (standard deviation) within each strata of the population. If NULL, sample variance of each strata will be used.

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

References

Gon<U+00E7>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

# NOT RUN {
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

# }