msaeDB (version 0.2.1)

saedb: EBLUPs under Univariate Fay Herriot Model with Difference Benchmarking

Description

This function produces EBLUPs, MSE, and aggregation of Univariate SAE with Difference Benchmarking

Usage

saedb(
  formula,
  vardir,
  weight,
  samevar = FALSE,
  MAXITER = 100,
  PRECISION = 1e-04,
  data
)

Arguments

formula

List of formula that describe the fitted model

vardir

Sampling variances of direct estimations,if it is included in data frame so it is the vector with the name of sampling variances.if it is not, it is a data frame of sampling variance in order : var1, cov12,.,cov1r,var2,cov23,.,cov2r,.,cov(r-1)(r),var(r)

weight

Known proportion of units in small areas, where \(\sum_{d=1}^{D}\) \(W_{rd}\) = 1 . d = 1...D is the number of small areas, and r = 1...R is the number of response variables

samevar

Whether the variances of the data are same or not. Logical input with default FALSE

MAXITER

Maximum number of iteration in Fisher-scoring algorithm with default 100

PRECISION

Limit of Fisher-scoring convergence tolerance with default 1e-4

data

The data frame

Value

This function returns a list of the following objects:

SAE_Eblup

A dataframe with the values of the EBLUPs estimators

MSE_Eblup

A dataframe with the values of estimated mean square errors of EBLUPs estimators

randomEffect

A dataframe with the values of the random effect estimators

Rmatrix

A block diagonal matrix composed of sampling errors

fit

A list containing the following objects:

method : The fitting method (this function is using "REML") convergence : The convergence result of Fisher-scoring algorithm (Logical Value) iterations : The number of Fisher-Scoring algorithm iterations estcoef : A dataframe with the estimated model coefficient, standard error,t statistics, p-values of the significance of each coefficient refvar : A dataframe with estimated random effect variances informationFisher : A matrix of information fisher from Fisher-scoring algorithm

difference_benchmarking

a list containing the following objects:

Estimation : A dataframe with the value of Benchmarked EBLUPs estimators Aggregation : The aggregation of benchmarked EBLUPs estimators, EBLUPs estimators and direct estimations MSE_DB : A dataframe with the values of estimated mean square errors of benchmarked EBLUPs estimators g.4a : First component of g4 in difference benchmarking MSE estimation formula g.4b : Second component of g4 in difference benchmarking MSE estimation formula

Examples

# NOT RUN {
##load dataset
data(datamsaeDB)

#Compute Fitted model for Y1, Y2, and Y3
#Y1 ~ X1 + X2
#Y2 ~ X2
#Y3 ~ X1

##Using parameter 'data'
formula = list(f1 = Y1~X1+X2,
               f2 = Y2~X2,
               f3 = Y3~X1)
vardir = c("v1","v12","v13","v2","v23","v3")
#Note : in real data for univariate SAE, if you does not have the values of covariances,
#       set covariancse as zero in the dataframe

weight = c("w1","w2","w3")
saeDB <- saedb(formula, vardir, weight, data=datamsaeDB)

#to calculate only one response variable
saeDB1 <- saedb(formula=list(f1=Y1~X1+X2),vardir ="v1", weight="w1",data=datamsaeDB )

##Do not use parameter 'data'
formula = list(f1 = datamsaeDB$Y1~datamsaeDB$X1+datamsaeDB$X2,
               f2 = datamsaeDB$Y2~datamsaeDB$X2,
               f3 = datamsaeDB$Y3~datamsaeDB$X1)
vardir = datamsaeDB[,c("v1","v12","v13","v2","v23","v3")]
#Note : in real data for univariate SAE, if you does not have the values of covariances,
#       set covariancse as zero in the dataframe
weight = datamsaeDB[,c("w1","w2","w3")]
saeDB_d <- saedb(formula, vardir, weight = weight)


saeDB$SAE_Eblup        #to see EBLUP Estimators
saeDB$MSE_Eblup        #to see estimated MSE of EBLUP estimators
saeDB$difference_benchmarking$Estimation   #to see Benchmarked EBLUP Estimators
saeDB$difference_benchmarking$MSE_DB       #to see estimated MSE of Benchmarked EBLUP Estimators
saeDB$difference_benchmarking$Aggregation  #to see the aggregation of, benchmarking





# }