| Title: | Small Area Estimation using Empirical Bayes without Auxiliary Variable |
| Version: | 0.1.0 |
| Description: | Estimates the parameter of small area in binary data without auxiliary variable using Empirical Bayes technique, mainly from Rao and Molina (2015,ISBN:9781118735787) with book entitled "Small Area Estimation Second Edition". This package provides another option of direct estimation using weight. This package also features alpha and beta parameter estimation on calculating process of small area. Those methods are Newton-Raphson and Moment which based on Wilcox (1979) <doi:10.1177/001316447903900302> and Kleinman (1973) <doi:10.1080/01621459.1973.10481332>. |
| License: | GPL (≥ 3) |
| Encoding: | UTF-8 |
| RoxygenNote: | 7.2.1 |
| Suggests: | knitr, rmarkdown |
| VignetteBuilder: | knitr |
| Imports: | descr, dplyr, rlang, stats |
| Depends: | R (≥ 3.5.0) |
| LazyData: | true |
| NeedsCompilation: | no |
| Packaged: | 2022-09-02 02:31:53 UTC; bps |
| Author: | Siti Rafika Fiandasari [aut, cre], Margaretha Ari Anggorowati [aut], Bahrul Ilmi Nasution [aut] |
| Maintainer: | Siti Rafika Fiandasari <fikafianda@gmail.com> |
| Repository: | CRAN |
| Date/Publication: | 2022-09-05 07:20:05 UTC |
Small Area Estimation method with Empirical Bayes and its RRMSE value by Naive Method
Description
Small Area Estimation method with Empirical Bayes and its RRMSE value by Naive Method
Usage
EBnaive(data, method, opt, maxiter = 100, tol = 1e-05)
Arguments
data |
the data must contain two or three columns : code, y, and weight data if exist. |
method |
Method to estimate alpha and beta parameter according to person(rao or claire) |
opt |
Method to estimate alpha and beta parameter according to the way of calculation (moment or nr) |
maxiter |
the Maximum iteration value with default 100 |
tol |
Tolerance error value at iteration with default 0.00001 |
Value
This function returns a list with following objects :
finalres |
an information about direct estimatior and EB estimator in each area |
estimation |
an information about EB estimator and its RRMSE value obtained by Naive method |
parameter |
Alpha and beta estimator |
pcap |
pcap (the weighted sample mean), vardir (the weighted sample variance),yt (the total number of the "success" category from each area), and nt (the total number of sample from each area) |
dir.est |
an information about direct estimator |
Examples
## load dataset with no weight value
data(dataEB)
## Calculates EB estimator
## with its RRMSE value by Naive method.
## Its alpha and beta estimator obtained
## by Moment method by J.N.K.Rao
EBnaive(data = dataEB[,-c(3)],method = "rao",opt = "moment", maxiter = 100, tol = 1e-5)
##load dataset with weight value
data(dataEB)
## Calculates EB estimator
## with its RRMSE value by Naive method.
## Its alpha and beta estimator obtained
## by Moment method by Claire E.B.O.
EBnaive(data = dataEB, method = "claire",opt = "moment", maxiter = 100, tol = 1e-5)
Estimates alpha and beta parameter to obtain EB estimator
Description
Estimates alpha and beta parameter to obtain EB estimator
Usage
alphabetaEB(data.dir, pcap, method, opt, maxiter, tol)
Arguments
data.dir |
Direct estimates of the data from function pcapdir |
pcap |
weighted sample mean and variance from function pcapdir |
method |
Method to estimate alpha and beta parameter according to person(rao or claire) |
opt |
Method to estimate alpha and beta parameter according to the way of calculation (moment or nr) |
maxiter |
the Maximum iteration value |
tol |
Tolerance error value at iteration |
Value
This function returns a data frame with following objects :
alpha_cap |
an alpha estimator by user's choice method |
beta_cap |
an beta estimator by user's choice method |
Examples
## load dataset with no weight value
data(dataEB)
temp = pcapdir(dataEB[,-c(3)])
## estimates alpha and beta parameter
## in EB estimate with Moment method by J.N.K.Rao
alphabetaEB(data.dir = temp$direst ,pcap = temp$pcap,
method = "rao", opt = "moment",maxiter = 100,tol = 0.00001)
##load dataset with weight value
data(dataEB)
temp = pcapdir(dataEB)
## estimates alpha and beta parameter
## in EB estimate with Moment method by Claire E.B.O.
alphabetaEB(data.dir = temp$direst ,pcap = temp$pcap,
method = "claire", opt = "moment",maxiter = 100,tol = 0.00001)
Small Area Estimation method with Empirical Bayes and its RRMSE value by Bootstrap Method
Description
Small Area Estimation method with Empirical Bayes and its RRMSE value by Bootstrap Method
Usage
bootstrapEB(data, method, opt, seed = NA, maxiter = 25, tol = 1e-05, B = 50)
Arguments
data |
the data must contain two or three columns : code, y, and weight data if exist. |
method |
Method to estimate alpha and beta parameter according to person(rao or claire) |
opt |
Method to estimate alpha and beta parameter according to the way of calculation (moment or nr) |
seed |
Setting a seed in set.seed() function to initialize a pseudorandom number generator with default number 0 |
maxiter |
the Maximum iteration value with default 100 |
tol |
Tolerance error value at iteration with default 0.00001 |
B |
The number of iteration of bootstrap resampling with default 200 |
Value
This function returns a list with following objects :
finalres |
an information about direct estimator and EB estimator in each area with its RRMSE value obtained by bootstrap method |
eb.estimation |
an information about EB estimator in each area with its RRMSE value obtained by Naive method |
References
Rao J, Peralta IM (2015). Small Area Estimation Second Edition. John Wiley & Sons, Inc.,Hoboken, New Jersey, Canada. ISBN 978-1-118-73578-7.
Examples
## load dataset with no weight value
data(dataEB)
## Calculates EB estimator with its
## RRMSE value by Bootstrap method.
## Its alpha and beta estimator obtained
## by Moment method by J.N.K.Rao
bootstrapEB(data = dataEB[,-c(3)], method = "rao",
opt = "moment", maxiter = 20, tol = 1e-5,B=20,seed=0)
##load dataset with weight value
data(dataEB)
## Calculates EB estimator with its
## RRMSE value by Bootstrap method.
## Its alpha and beta estimator obtained
## by Moment method by Claire E.B.O.
bootstrapEB(data = dataEB, method = "rao",
opt = "moment", maxiter = 20, tol = 1e-5,B=20,seed=0)
Sample Data for Practice
Description
An example data for trying and testing in saebnocov package
Usage
dataEB
Format
A sample data has 3 column, which are:
- code
code of each area
- y
status "success" or not in each unit sample of each area
- weight
a weight value in each unit sample of each area
Examples
data(dataEB)
Small Area Estimation method with Empirical Bayes and its RRMSE value by Naive Method
Description
Small Area Estimation method with Empirical Bayes and its RRMSE value by Naive Method
Usage
estEBnaive(data.dir, pcap, param)
Arguments
data.dir |
direct estimator information from function direct.est |
pcap |
pcap (the weighted sample mean), vardir (the weighted sample variance),yt (the total number of the "success" category from each area), and nt (the total number of sample from each area) |
param |
Alpha and Beta estimator |
Value
This function returns a list with following objects :
eb.est |
EB estimator in each area |
mse |
MSE of EB estimator obtained by Naive method |
rrmse |
RRMSE of EB estimator obtained by Naive method |
Examples
## load dataset with no weight value
data(dataEB)
temp = pcapdir(dataEB[,-c(3)])
## estimates alpha and beta parameter
## in EB estimate with Moment method by J.N.K.Rao
temp1 = alphabetaEB(data.dir = temp$direst ,pcap = temp$pcap,
method = "rao", opt = "moment",
maxiter = 100,tol = 0.00001)
## calculates EB estimator
## and its MSE by naive method
estEBnaive(data.dir = temp$direst, pcap = temp$pcap, param = temp1)
Small Area Estimation method with Empirical Bayes and its RRMSE value by Jackknife Method
Description
Small Area Estimation method with Empirical Bayes and its RRMSE value by Jackknife Method
Usage
jackknifeEB(data, method, opt, maxiter = 100, tol = 1e-05)
Arguments
data |
the data must contain two or three columns : code, y, and weight data if exist. |
method |
Method to estimate alpha and beta parameter according to person(rao or claire) |
opt |
Method to estimate alpha and beta parameter according to the way of calculation (moment or nr) |
maxiter |
the Maximum iteration value with default 100 |
tol |
Tolerance error value at iteration with default 0.00001 |
Value
This function returns a list with following objects :
finalres |
an information about direct estimator and EB estimator in each area with its RRMSE value obtained by jackknife method |
eb.estimation |
an information about EB estimator in each area with its RRMSE value obtained by Naive method |
Examples
## load dataset with no weight value
data(dataEB)
## Calculates EB estimator with
## its RRMSE value by Jackknife method.
## Its alpha and beta estimator obtained
## by Moment method by J.N.K.Rao
jackknifeEB(data = dataEB[,-c(3)], method = "rao",
opt = "moment", maxiter = 20, tol = 1e-5)
##load dataset with weight value
data(dataEB)
## Calculates EB estimator with
## its RRMSE value by Jackknife method.
## Its alpha and beta estimator obtained
## by Moment method by Claire E.B.O.
jackknifeEB(data = dataEB, method = "rao",
opt = "moment", maxiter = 20, tol = 1e-5)
Matrix G in Newton Raphson method by Claire E.B.O.
Description
Matrix G in Newton Raphson method by Claire E.B.O.
Usage
matrixClaire(alpha, beta)
Arguments
alpha |
An alpha estimate value on iterating process |
beta |
A beta estimate value on iterating process |
Value
This function returns a value of matrix G.
Examples
## load dataset with no weight value
data(dataEB)
temp = pcapdir(dataEB[,-c(3)])
## estimates alpha and beta parameter
## in EB estimate with Moment method by J.N.K.Rao
temp1 = alphabetaEB(data.dir = temp$direst ,pcap = temp$pcap,
method = "rao", opt = "moment",
maxiter = 100,tol = 0.00001)
##calculates matrix G
matrixClaire(alpha = temp1$alpha_cap, beta = temp1$beta_cap)
Matrix G in Newton Raphson method by J.N.K.Rao
Description
Matrix G in Newton Raphson method by J.N.K.Rao
Usage
matrixRao(alpha, beta, ni, yi)
Arguments
alpha |
An alpha estimate value on iterating process |
beta |
A beta estimate value on iterating process |
ni |
The number of sample in each area |
yi |
The number of "success" value in each area |
Value
This function returns a value of matrix G.
Examples
## load dataset with no weight value
data(dataEB)
temp = pcapdir(dataEB[,-c(3)])
## estimates alpha and beta parameter
## in EB estimate with Moment method by J.N.K.Rao
temp1 = alphabetaEB(data.dir = temp$direst ,pcap = temp$pcap,
method = "rao", opt = "moment",
maxiter = 100,tol = 0.00001)
##calculates matrix G
matrixRao(alpha = temp1$alpha_cap,
beta = temp1$beta_cap, ni = temp$direst$ni,
yi = temp$direst$yi)
Estimates alpha and beta parameter with Moment method by Claire E.B.O.
Description
Estimates alpha and beta parameter with Moment method by Claire E.B.O.
Usage
momentClaire(data.dir, pcap)
Arguments
data.dir |
Direct estimates of the data from function pcapdir |
pcap |
weighted sample mean and variance from function pcapdir |
Value
This function returns a data frame with following objects :
alpha_cap |
an alpha estimator by Moment method of Claire E.B.O. |
beta_cap |
a beta estimator by Moment method of Claire E.B.O. |
Examples
## load dataset with no weight value
data(dataEB)
temp = pcapdir(dataEB[,-c(3)])
momentClaire(data.dir = temp$direst, pcap = temp$pcap)
##load dataset with weight value
data(dataEB)
temp = pcapdir(dataEB[,-c(3)])
momentClaire(data.dir = temp$direst, pcap = temp$pcap)
Estimates alpha and beta parameter with Moment method by J.N.K.Rao
Description
Estimates alpha and beta parameter with Moment method by J.N.K.Rao
Usage
momentRao(data.dir, pcap)
Arguments
data.dir |
Direct estimates of the data from function pcapdir |
pcap |
weighted sample mean and variance from function pcapdir |
Value
This function returns a data frame with following objects :
alpha_cap |
an alpha estimator by Moment method of Claire E.B.O. |
beta_cap |
an beta estimator by Moment method of Claire E.B.O. |
Examples
## load dataset with no weight value
data(dataEB)
temp = pcapdir(dataEB[,-c(3)])
momentRao(data.dir = temp$direst, pcap = temp$pcap)
##load dataset with weight value
data(dataEB)
temp = pcapdir(dataEB[,-c(3)])
momentRao(data.dir = temp$direst, pcap = temp$pcap)
Estimates alpha and beta parameter with Newton Raphson method by Claire E.B.O.
Description
Estimates alpha and beta parameter with Newton Raphson method by Claire E.B.O.
Usage
newtonRaphsonC(data.dir, pcap, maxiter, tol)
Arguments
data.dir |
Direct estimates of the data from function pcapdir |
pcap |
weighted sample mean and variance from function pcapdir |
maxiter |
the Maximum iteration value |
tol |
Tolerance error value in iteration |
Value
This function returns a data frame with following objects :
alpha_cap |
an alpha estimator by Newton Raphson method of Claire E.B.O. |
beta_cap |
an beta estimator by Newton Raphson method of Claire E.B.O. |
Examples
## load dataset with no weight value
data(dataEB)
temp = pcapdir(dataEB[,-c(3)])
newtonRaphsonC(data.dir = temp$direst, pcap = temp$pcap,
maxiter = 100, tol = 0.00001)
##load dataset with weight value
data(dataEB)
temp = pcapdir(dataEB[,-c(3)])
newtonRaphsonC(data.dir = temp$direst, pcap = temp$pcap,
maxiter = 100, tol = 0.00001)
Estimates alpha and beta parameter with Newton Raphson method by J.N.K. Rao
Description
Estimates alpha and beta parameter with Newton Raphson method by J.N.K. Rao
Usage
newtonRaphsonR(data.dir, pcap, maxiter, tol)
Arguments
data.dir |
Direct estimates of the data from function pcapdir |
pcap |
weighted sample mean and variance from function pcapdir |
maxiter |
the Maximum iteration value |
tol |
Tolerance error value in iteration |
Value
This function returns a data frame with following objects :
alpha_cap |
an alpha estimator by Newton Raphson method of J.N.K.Rao |
beta_cap |
an beta estimator by Newton Raphson method of J.N.K.Rao |
Examples
## load dataset with no weight value
data(dataEB)
temp = pcapdir(dataEB[,-c(3)])
newtonRaphsonR(data.dir = temp$direst, pcap = temp$pcap,
maxiter = 100, tol = 0.00001)
##load dataset with weight value
data(dataEB)
temp = pcapdir(dataEB)
newtonRaphsonR(data.dir = temp$direst, pcap = temp$pcap,
maxiter = 100, tol = 0.00001)
Weighted Sample Mean and Variance
Description
Weighted Sample Mean and Variance
Usage
pcapdir(data)
Arguments
data |
the data must contain two or three columns : code, y, and weight data if exist. |
Value
This function returns a list with following objects :
direst |
an information about direct estimatior in each area |
pcap |
pcap (the weighted sample mean), vardir (the weighted sample variance),yt (the total number of the "success" category from each area), and nt (the total number of sample from each area) |
Examples
## load dataset with no weight value
data(dataEB)
pcapdir(dataEB[,-c(3)])
##load dataset with weight value
data(dataEB)
pcapdir(dataEB)
Vector g in Newton Raphson Method by Claire E.B.O.
Description
Vector g in Newton Raphson Method by Claire E.B.O.
Usage
vectorClaire(alpha, beta, p)
Arguments
alpha |
An alpha estimate value on iterating process |
beta |
A beta estimate value on iterating process |
p |
direct estimator or proportion value |
Value
This function returns a value of vector g.
Examples
## load dataset with no weight value
data(dataEB)
temp = pcapdir(dataEB[,-c(3)])
## estimates alpha and beta parameter
## in EB estimate with Moment method by J.N.K.Rao
temp1 = alphabetaEB(data.dir = temp$direst ,pcap = temp$pcap,
method = "rao", opt = "moment",
maxiter = 100,tol = 0.00001)
##calculates vector g
vectorClaire(alpha = temp1$alpha_cap, beta = temp1$beta_cap, p = temp$direst$p)
Vector g in Newton Raphson Method by J.N.K.Rao
Description
Vector g in Newton Raphson Method by J.N.K.Rao
Usage
vectorRao(alpha, beta, ni, yi)
Arguments
alpha |
An alpha estimate value on iterating process |
beta |
A beta estimate value on iterating process |
ni |
The number of sample in each area |
yi |
The number of "success" value in each area |
Value
This function returns a value of vector g.
Examples
## load dataset with no weight value
data(dataEB)
temp = pcapdir(dataEB[,-c(3)])
## estimates alpha and beta parameter
## in EB estimate with Moment method by J.N.K.Rao
temp1 = alphabetaEB(data.dir = temp$direst ,pcap = temp$pcap,
method = "rao", opt = "moment",
maxiter = 100,tol = 0.00001)
##calculates vector g
vectorRao(alpha = temp1$alpha_cap, beta = temp1$beta_cap,
ni = temp$direst$ni, yi = temp$direst$yi)