eblupfh_ns {saens}R Documentation

Synthetic Estimator.

Description

Synthetic estimator is one of the simple methods to obtain predicted values of mean specific area parameters, which the direct estimates are unknown. Based on estimated of parameter coefficient models using Empirical Best Unbiased Prediction (EBLUP), the synthetic estimator is obtained by calibrating the estimated parameter coefficient to the auxiliary variables.

Usage

eblupfh_ns(
  formula,
  data,
  vardir,
  method = "REML",
  maxiter = 100,
  precision = 1e-04,
  scale = FALSE,
  print_result = TRUE
)

Arguments

formula

an object of class formula that contains a description of the model to be fitted. The variables included in the formula must be contained in the data.

data

a data frame or a data frame extension (e.g. a tibble).

vardir

vector or column names from data that contain variance sampling from the direct estimator for each area.

method

Fitting method can be chosen between 'ML' and 'REML'

maxiter

maximum number of iterations allowed in the Fisher-scoring algorithm. Default is 100 iterations.

precision

convergence tolerance limit for the Fisher-scoring algorithm. Default value is 0.0001.

scale

scaling auxiliary variable or not, default value is FALSE.

print_result

print coefficient or not, default value is TRUE.

Details

The model is defined as response ~ auxiliary variables, where the response variable, of numeric type, may contain NA values. When the response variable contains NA, it will be estimated using a synthetic estimator.

Value

The function returns a list with the following objects df_res and fit: df_res a data frame that contains the following columns:

fit a list containing the following objects:

References

  1. Rao, J. N., & Molina, I. (2015). Small area estimation. John Wiley & Sons.

Examples

library(saens)

m1 <- eblupfh_ns(y ~ x1 + x2 + x3, data = mys, vardir = "var")
m1 <- eblupfh_ns(y ~ x1 + x2 + x3, data = mys, vardir = ~var)


[Package saens version 0.1.2 Index]