FHme: Fay-Herriot Model with Measurement Error

View source: R/FHme.R

FHmeR Documentation

Fay-Herriot Model with Measurement Error

Description

This function gives the EBLUP estimator based on Fay-Herriot model with measurement error.

Usage

FHme(
  formula,
  vardir,
  var.x,
  type.x = "witherror",
  MAXITER = 1000,
  PRECISION = 1e-04,
  data
)

Arguments

formula

an object of class formula (or one that can be coerced to that class): a symbolic description of the model to be fitted. The variables included formula must have a length equal to the number of domains m. This formula can provide auxiliary variable either measured with error or without error or combination between them. If the auxiliary variable are combination between noerror and witherror variable, input all witherror variable first then noerror variable.

vardir

vector containing the m sampling variances of direct estimators for each domain. The values must be sorted as the Y.

var.x

vector containing mean squared error of X . The values must be sorted as the X. if you use optional data, input this parameter use c(""), example: var.x = c("c1") or var.x = c("c1","c2").

type.x

type of auxiliary variable used in the model. Either source measured with noerror, witherror and mix. Default value is witherror.

MAXITER

maximum number of iterations allowed. Default value is 1000 iterations.

PRECISION

convergence tolerance limit. Default value is 0.0001.

data

optional data frame containing the variables named in formula, vardir, and var.x.

Details

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.

Value

The function returns a list with the following objects:

eblup

vector with the values of the estimators for the domains.

fit

a list containing the following objects:

  • method : type of fitting method.

  • convergence : a logical value of convergence when calculating estimated beta and estimated random effects.

  • iterations : number of iterations when calculating estimated beta and estimated random effects.

  • estcoef : a data frame with the estimated model coefficient (beta) in the first column, their standard error (std.error) in the second column, the t-statistics (t.statistics) in the third column, and the p-values of the significance of each coefficient (pvalue) in the last column.

  • refvar : a value of estimated random effects.

  • gamma : vector with values of the estimated gamma for each domains.

See Also

mse_FHme

Examples

data(dataME)
data(datamix)
sae.me <- FHme(formula = y ~ x.hat, vardir = vardir, var.x = c("var.x"), data = dataME)
sae.mix <- FHme(formula = y ~ x.hat1 + x.hat2 + x3 + x4,
            vardir = vardir, var.x = c("var.x1", "var.x2"), type.x = "mix", data = datamix)


saeME documentation built on April 21, 2022, 5:15 p.m.