Description Usage Arguments Value Examples
Calculates the parametric bootstrap mean squared error estimates of ratio benchmarking for univariate non sampled area in small area estimation
1 2 3 4 5 6 7 8 9 10 11 | mse_saeRBns(
formula,
vardir,
weight,
cluster,
samevar = FALSE,
B = 1000,
MAXITER = 100,
PRECISION = 1e-04,
data
)
|
formula |
an object of class list of formula describe the fitted model |
vardir |
vector containing sampling variances of direct estimators |
weight |
vector containing proportion of units in small areas |
cluster |
vector containing cluster of auxiliary variable |
samevar |
logical. If |
B |
number of bootstrap. Default is 1000 |
MAXITER |
maximum number of iterations for Fisher-scoring. Default is 100 |
PRECISION |
coverage tolerance limit for the Fisher Scoring algorithm. Default value is |
data |
dataframe containing the variables named in formula, vardir, and weight |
mse.eblup |
estimated mean squared errors of the EBLUPs for the small domains based on Prasad Rao |
pbmse.eblupRB |
parametric bootstrap mean squared error estimates of the ratio benchmark |
running.time |
time for running function |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 | ## load dataset
data(datamsaeRBns)
# Compute MSE EBLUP and Ratio Benchmark
## Using parameter 'data'
mse_sae = mse_saeRBns(Y1 ~ X1 + X2, v1, w1, c1, data = datamsaeRBns)
## Without parameter 'data'
mse_sae = mse_saeRBns(datamsaeRBns$Y1 ~ datamsaeRBns$X1 + datamsaeRBns$X2,
datamsaeRBns$v1, datamsaeRBns$w1, datamsaeRBns$c1)
## Return
mse_sae$pbmse.eblupRB # to see the MSE Ratio Benchmark estimators
|
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