BLE_Ratio {BayesSampling} | R Documentation |
Creates the Bayes Linear Estimator for the Ratio "estimator"
BLE_Ratio(ys, xs, x_nots, m = NULL, v = NULL, sigma = NULL, n = NULL)
ys |
vector of sample observations or sample mean ( |
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 |
v |
prior variance of the ratio between Y and X (bigger than |
sigma |
prior estimate of variability (standard deviation) of the ratio within the population. If |
n |
sample size. Necessary only if |
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
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.
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