mseFH.uprop | R Documentation |
This function gives the MSE of transformed EBLUP and Empirical Best Predictor (EBP) based on a univariate Fay-Herriot model with modified parametric bootstrap approach proposed by Butar & Lahiri.
mseFH.uprop(
formula,
vardir,
MAXITER = 100,
PRECISION = 1e-04,
L = 1000,
B = 1000,
data
)
formula |
an object of class |
vardir |
vector containing the sampling variances of direct estimators for each domain. The values must be sorted as the variables in |
MAXITER |
maximum number of iterations allowed in the Fisher-scoring algorithm, Default: |
PRECISION |
convergence tolerance limit for the Fisher-scoring algorithm, Default: |
L |
number of Monte Carlo iterations in calculating Empirical Best Predictor (EBP), Default: |
B |
number of Bootstrap iterations in calculating MSE, Default: |
data |
optional data frame containing the variables named in |
The function returns a list with the following objects:
est |
a data frame containing values of the estimators for each domains. |
PC
: transformed EBLUP estimators using inverse alr.
EBP
: Empirical Best Predictor using Monte Carlo.
fit |
a list containing the following objects (model is fitted using REML): |
convergence
: a logical value equal to TRUE
if Fisher-scoring algorithm converges in less than MAXITER
iterations.
iterations
: number of iterations performed by the Fisher-scoring algorithm.
estcoef
: a data frame that contains the estimated model coefficients, standard errors, t-statistics, and p-values of each coefficient.
refvar
: estimated random effects variance.
components |
a data frame containing the following columns: |
random.effects
: estimated random effect values of the fitted model.
residuals
: residuals of the fitted model.
mse |
a data frame containing estimated MSE of the estimators. |
PC
: estimated MSE of plugin (PC) estimators.
EBP
: estimated MSE of EBP estimators.
## Not run:
## Load dataset
data(datasaeu)
## If data is defined
Fo = y ~ x1 + x2
vardir = "vardir"
MSE.data <- mseFH.uprop(Fo, vardir, data = datasaeu)
## If data is undefined
Fo = datasaeu$y ~ datasaeu$x1 + datasaeu$x2
vardir = datasaeu$vardir
MSE <- mseFH.uprop(Fo, vardir)
## See the estimators
MSE$mse
## End(Not run)
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