BLE_SRS {BayesSampling} R Documentation

## Simple Random Sample BLE

### Description

Creates the Bayes Linear Estimator for the Simple Random Sampling design (without replacement)

### Usage

BLE_SRS(ys, N, m = NULL, v = NULL, sigma = NULL, n = NULL)


### Arguments

 ys vector of sample observations or sample mean (sigma and n parameters will be required in this case). N total size of the population. m prior mean. If NULL, sample mean will be used (non-informative prior). v prior variance of an element from the population (bigger than sigma^2). If NULL, it will tend to infinity (non-informative prior). sigma prior estimate of variability (standard deviation) within the population. If NULL, sample variance will be used. n sample size. Necessary only if ys represent sample mean (will not be used otherwise).

### Value

A list containing the following components:

• est.beta - BLE of Beta (BLE for every individual)

• 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

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

ys <- c(5,6,8)
N <- 5
m <- 6
v <- 5
sigma <- 1

Estimator <- BLE_SRS(ys, N, m, v, sigma)
Estimator

# Same example but informing sample mean and sample size instead of sample observations
ys <- mean(c(5,6,8))
N <- 5
n <- 3
m <- 6
v <- 5
sigma <- 1

Estimator <- BLE_SRS(ys, N, m, v, sigma, n)
Estimator



[Package BayesSampling version 1.1.0 Index]