BLE_Categorical {BayesSampling} R Documentation

## Bayes Linear Method for Categorical Data

### Description

Creates the Bayes Linear Estimator for Categorical Data

### Usage

```BLE_Categorical(ys, n, N, m = NULL, rho = NULL)
```

### Arguments

 `ys` k-vector of sample proportion for each category. `n` sample size. `N` total size of the population. `m` k-vector with the prior proportion of each strata. If `NULL`, sample proportion for each strata will be used (non-informative prior). `rho` matrix with the prior correlation coefficients between two different units within categories. It must be a symmetric square matrix of dimension k (or k-1). If `NULL`, non-informative prior will be used.

### Value

A list containing the following components:

• `est.prop` - BLE for the sample proportion of each category

• `Vest.prop` - Variance associated with the above

• `Vs.Matrix` - Vs matrix, as defined by the BLE method (should be a positive-definite matrix)

• `R.Matrix` - R matrix, as defined by the BLE method (should be a positive-definite matrix)

### 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

```# 2 categories
ys <- c(0.2614, 0.7386)
n <- 153
N <- 15288
m <- c(0.7, 0.3)
rho <- matrix(0.1, 1)

Estimator <- BLE_Categorical(ys,n,N,m,rho)
Estimator

ys <- c(0.2614, 0.7386)
n <- 153
N <- 15288
m <- c(0.7, 0.3)
rho <- matrix(0.5, 1)

Estimator <- BLE_Categorical(ys,n,N,m,rho)
Estimator

# 3 categories
ys <- c(0.2, 0.5, 0.3)
n <- 100
N <- 10000
m <- c(0.4, 0.1, 0.5)
mat <- c(0.4, 0.1, 0.1, 0.1, 0.2, 0.1, 0.1, 0.1, 0.6)
rho <- matrix(mat, 3, 3)

```

[Package BayesSampling version 1.1.0 Index]