BLE_Ratio: Ratio BLE
In BayesSampling: Bayes Linear Estimators for Finite Population

Description Usage Arguments Value Source References Examples

Creates the Bayes Linear Estimator for the Ratio "estimator"

1	BLE_Ratio(ys, xs, x_nots, m = NULL, v = NULL, sigma = NULL, n = NULL)

`ys`	vector of sample observations or sample mean (`sigma` and `n` parameters will be required in this case).
`xs`	vector with values for the auxiliary variable of the elements in the sample or sample mean.
`x_nots`	vector with values for the auxiliary variable of the elements not in the sample.
`m`	prior mean for the ratio between Y and X. If `NULL`, `mean(ys)/mean(xs)` will be used (non-informative prior).
`v`	prior variance of the ratio between Y and X (bigger than `sigma^2`). If `NULL`, it will tend to infinity (non-informative prior).
`sigma`	prior estimate of variability (standard deviation) of the ratio within the population. If `NULL`, sample variance of the ratio will be used.
`n`	sample size. Necessary only if `ys` and `xs` represent sample means (will not be used otherwise).

A list containing the following components:

est.beta - BLE of Beta
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

https://www150.statcan.gc.ca/n1/en/catalogue/12-001-X201400111886

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.

ys <- c(10,8,6)
xs <- c(5,4,3.1)
x_nots <- c(1,20,13,15,-5)
m <- 2.5
v <- 10
sigma <- 2

Estimator <- BLE_Ratio(ys, xs, x_nots, m, v, sigma)
Estimator


# Same example but informing sample means and sample size instead of sample observations
ys <- mean(c(10,8,6))
xs <- mean(c(5,4,3.1))
n <- 3
x_nots <- c(1,20,13,15,-5)
m <- 2.5
v <- 10
sigma <- 2

Estimator <- BLE_Ratio(ys, xs, x_nots, m, v, sigma, n)
Estimator