saeFH.uprop {sae.prop} | R Documentation |
EBLUPs based on a Univariate Fay Herriot model with Additive Logistic Transformation
Description
This function gives the transformed EBLUP and Empirical Best Predictor (EBP) based on a univariate Fay-Herriot model.
Usage
saeFH.uprop(formula, vardir, MAXITER = 100, PRECISION = 1e-04, L = 1000, data)
Arguments
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: |
data |
optional data frame containing the variables named in |
Value
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 toTRUE
if Fisher-scoring algorithm converges in less thanMAXITER
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.
Examples
## Not run:
## Load dataset
data(datasaeu)
## If data is defined
Fo = y ~ x1 + x2
vardir = "vardir"
model.data <- saeFH.uprop(Fo, vardir, data = datasaeu)
## If data is undefined
Fo = datasaeu$y ~ datasaeu$x1 + datasaeu$x2
vardir = datasaeu$vardir
model <- saeFH.uprop(Fo, vardir)
## See the estimators
model$est
## End(Not run)