saefh: EBLUPs under Univariate Fay Herriot Model
In msaeDB: Difference Benchmarking for Multivariate Small Area Estimation

Description Usage Arguments Value Examples

This function produces EBLUPs, MSE of Univariate SAE

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

`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

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

##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

saeFH <- saefh(formula, vardir, data=datamsaeDB)

#to calculate only one response variable
saeFH1 <- saefh(formula=list(f1=Y1~X1+X2),vardir ="v1",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
saeFH_d <- saefh(formula, vardir)

saeFH$SAE_Eblup       #to see EBLUP Estimators
saeFH$MSE_Eblup        #to see estimated MSE of EBLUP estimators