mseFH.ns.uprop {sae.prop}

Parametric Bootstrap Mean Squared Error of EBLUPs based on a Univariate Fay Herriot model with Additive Logistic Transformation for Non-Sampled Data

Description

This function gives the MSE of transformed EBLUP based on a univariate Fay-Herriot model. For sampled domains, MSE is estimated using modified parametric bootstrap approach proposed by Butar & Lahiri. For non-sampled domains, MSE is estimated using modified approach proposed by Haris & Ubaidillah.

Usage

mseFH.ns.uprop(
  formula,
  vardir,
  MAXITER = 100,
  PRECISION = 1e-04,
  cluster = "auto",
  B = 1000,
  data
)

Arguments

formula

an object of class formula that describe the fitted model.

vardir

vector containing the sampling variances of direct estimators for each domain. The values must be sorted as the variables in formula.

MAXITER

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

PRECISION

convergence tolerance limit for the Fisher-scoring algorithm, Default: 1e-4.

cluster

Default: "auto". If cluster = "auto", then the clustering will be performed by the function by finding optimal number of cluster. If cluster is a number, then clustering will be performed based on the chosen number of cluster. If cluster is a vector containing cluster information, then the vector will be used directly to find average of random effects. Clustering is performed with k-medoids algorithms using the function pamk. If "auto" is chosen, krange are set to 2:(nrow(data)-1).

B

number of Bootstrap iterations in calculating MSE, Default: 1000.

data

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

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.

• status : status of corresponding domain, whether sampled or non-sampled.

• cluster : cluster of corresponding domain.

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.

• cluster.information : a data frame containing average random effects of sampled domain in each cluster.

components

a data frame containing the following columns:

• random.effects : estimated random effect values of the fitted model.

• residuals : residuals of the fitted model.

• status : status of corresponding domain, whether sampled or non-sampled.

mse

a data frame containing estimated MSE of the estimators.

• PC : estimated MSE of plugin (PC) estimators.

• status : status of domain, whether sampled or non-sampled.

Examples

## Not run: 
## Load dataset
data(datasaeu.ns)

## If data is defined
Fo = y ~ x1 + x2
vardir = "vardir"
MSE.ns <- mseFH.ns.uprop(Fo, vardir, data = datasaeu.ns)

## If data is undefined (and option for cluster arguments)
Fo = datasaeu.ns$y ~ datasaeu.ns$x1 + datasaeu.ns$x2
vardir = datasaeu.ns$vardir

### "auto"
MSE.ns1 <- mseFH.ns.uprop(Fo, vardir, cluster = "auto")

### number of clusters
MSE.ns2 <- mseFH.ns.uprop(Fo, vardir, cluster = 2)

### vector containing cluster for each domain
MSE.ns3 <- mseFH.ns.uprop(Fo, vardir, cluster = datasaeu.ns$cluster)

## See the estimators
MSE.ns$mse

## End(Not run)