Description Usage Arguments Details Value See Also Examples
This function gives the area level EB and MSE estimator based on Wakefield (2006) model and the refinement model by Kismiantini (2007).
1 |
formula |
an object of class |
data |
a mandatory data frame containing the variables in |
e |
a variable that contains the expected numbers of cases in each area. |
A typical model has the form response ~ terms where the response is a vector with numeric data type and terms is a set(s) of auxiliary variables.
A formula has an implied intercept term. To remove this use either y ~ x - 1 or y ~ 0 + x. See formula
for more details of allowed formulae.
The formula
only accomodates variables with count data type and will be modeled using binomial negatif linear regression.
The function returns a list with the following objects:
data frame with number of rows equal to number of areas containing the EB estimator. For domains with zero sample size, the EB estimators are based on the synthetic regression.
alpha: the scale parameter estimator in Gamma distribution
v: the shape parameter estimator in Gamma distribution
Estimate: maximum likelihood estimator of the model parameters
SE: asymptotic estimate of the standard error of the the parameters
Z: the Z statistic of the asymptotic hypothesis test that the population value for the parameter is 0
LCL: lower 95% confidence interval for the parameter estimators
UCL: upper 95% confidence interval for the parameter estimators
method: Jackknife
mse: the mean squared error estimator of the EB estimators
est: direct estimators for the response variable
mse: the mean squared error estimator of the direct estimators
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