msaefh: EBLUPs under Multivariate Fay Herriot Model

In msaeDB: Difference Benchmarking for Multivariate Small Area Estimation

Description

This function produces EBLUPs, MSE of Multivariate SAE

Usage

msaefh(
  formula,
  vardir,
  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)
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:

MSAE_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

Examples

##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")
msaeFH <- msaefh(formula, vardir, 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")]
msaeFH_d <- msaefh(formula, vardir)

msaeFH$MSAE_Eblup       #to see EBLUP Estimators
msaeFH$MSE_Eblup        #to see estimated MSE of EBLUP estimators

msaeDB documentation built on April 8, 2021, 5:07 p.m.