msaedb: EBLUPs under Multivariate Fay Herriot Model with Difference...
In msaeDB: Difference Benchmarking for Multivariate Small Area Estimation

Description Usage Arguments Value Examples

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

msaedb(
  formula,
  vardir,
  weight,
  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)`
`weight`	Known proportion of units in small areas, where sum from d=1 to D of Wrd = 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

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

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

##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")
weight = c("w1","w2","w3")
msaeDB <- msaedb(formula, vardir, weight, 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")]
weight = datamsaeDB[,c("w1","w2","w3")]
msaeDB_d <- msaedb(formula, vardir, weight)

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