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

Gonçalves, 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]