Description Usage Arguments Value Source References Examples
View source: R/sample_designs.R
Creates the Bayes Linear Estimator for the Ratio "estimator"
1 
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/12001X201400111886
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, 1528.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22  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

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