Description Usage Arguments Value Examples
This function gives EBLUPs ratio benchmarking based on multivariate Fay-Herriot (Model 1)
1 2 3 4 5 6 7 8 9 | est_msaeRB(
formula,
vardir,
weight,
samevar = FALSE,
MAXITER = 100,
PRECISION = 1e-04,
data
)
|
formula |
an object of class list of formula describe the fitted models |
vardir |
matrix containing sampling variances of direct estimators. The order is: |
weight |
matrix containing proportion of units in small areas. The order is: |
samevar |
logical. If |
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 |
This function returns a list with following objects:
eblup |
a list containing a value of estimators |
est.eblup : a dataframe containing EBLUP estimators
est.eblupRB : a dataframe containing ratio benchmark estimators
fit |
a list contining following objects: |
method : fitting method, named "REML"
convergence : logical value of convergence of Fisher Scoring
iterations : number of iterations of Fisher Scoring algorithm
estcoef : a data frame containing estimated model coefficients (beta, std. error, t value, p-value
)
refvar : estimated random effect variance
random.effect |
a data frame containing values of random effect estimators |
agregation |
a data frame containing agregation of direct, EBLUP, and ratio benchmark estimation |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 | ## load dataset
data(datamsaeRB)
# Compute EBLUP and Ratio Benchmark using auxiliary variables X1 and X2 for each dependent variable
## Using parameter 'data'
Fo = list(f1 = Y1 ~ X1 + X2,
f2 = Y2 ~ X1 + X2,
f3 = Y3 ~ X1 + X2)
vardir = c("v1", "v12", "v13", "v2", "v23", "v3")
weight = c("w1", "w2", "w3")
est_msae = est_msaeRB(Fo, vardir, weight, data = datamsaeRB)
## Without parameter 'data'
Fo = list(f1 = datamsaeRB$Y1 ~ datamsaeRB$X1 + datamsaeRB$X2,
f2 = datamsaeRB$Y2 ~ datamsaeRB$X1 + datamsaeRB$X2,
f3 = datamsaeRB$Y3 ~ datamsaeRB$X1 + datamsaeRB$X2)
vardir = datamsaeRB[, c("v1", "v12", "v13", "v2", "v23", "v3")]
weight = datamsaeRB[, c("w1", "w2", "w3")]
est_msae = est_msaeRB(Fo, vardir, weight)
## Return
est_msae$eblup$est.eblupRB # to see the Ratio Benchmark estimators
|
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