saedbns: EBLUPs under Univariate 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 Univariate SAE with Difference Benchmarking for non-sampled area

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

`formula`	List of formula that describe the fitted model
`vardir`	Sampling variances of direct estimations included in data frame as the vector with the name of sampling variances 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
`cluster`	cluster information
`nonsample`	A column with logical values, `TRUE` if the area is non-sampled
`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_sampled`	A dataframe with the values of the EBLUPs estimators for sampled areas
`SAE_Eblup_all`	A dataframe with the values of the EBLUPs estimators for all areas
`MSE_Eblup_sampled`	A dataframe with the values of estimated mean square errors of EBLUPs estimators for sampled areas
`MSE_Eblup_all`	A dataframe with the values of estimated mean square errors of EBLUPs estimators for all areas
`randomEffect_sampled`	a dataframe with the values of the random effect estimators for sampled areas
`randomEffect_all`	a dataframe with the values of the random effect estimators for all areas
`Rmatrix_sampled`	a block diagonal matrix composed of sampling errors for sampled areas
`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_sampled : A dataframe with the values of benchmarked EBLUPs estimators for sampled areas
Estimation_all : A dataframe with the values of benchmarked EBLUPs estimators for all areas
Aggregation_sampled : The aggregation of benchmarked EBLUPs estimators, EBLUPs estimators and direct estimations for sampled areas
Aggregation_all : The aggregation of benchmarked EBLUPs estimators, EBLUPs estimators and direct estimations for all areas
MSE_DB_sampled : A dataframe with the values of estimated mean square errors of benchmarked EBLUPs estimators for sampled areas
MSE_DB_all : A dataframe with the values of estimated mean square errors of benchmarked EBLUPs estimators for all areas
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(datamsaeDBns)
#Note : Make sure yout dataset does not contain NA Values
#       you can set 0 in Direct estinations and vardir for non-sampled areas.

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

##Using parameter 'data'
formula = list(f1 = Y1~X1+X2,
               f2 = Y2~X1+X2,
               f3 = Y3~X1+X2)
vardir = c("v1","v12","v13","v2","v23","v3")
weight = c("w1","w2","w3")
cluster = c("clY1","clY2","clY3")
nonsample = "nonsample"
saeDBns <- saedbns(formula,vardir, weight,cluster, nonsample, data=datamsaeDBns)

#to calculate only one response variable
saeDB1 <- saedbns(formula=list(f1=Y1~X1+X2),vardir ="v1", weight="w1",
         cluster = "clY1",nonsample = "nonsample",data=datamsaeDBns )