| Type: | Package |
| Title: | Random Heteroscedastic Nested Error Regression |
| Version: | 1.1 |
| Author: | Shonosuke Sugasawa |
| Maintainer: | Shonosuke Sugasawa <shonosuke622@gmail.com> |
| Description: | Performs the random heteroscedastic nested error regression model described in Kubokawa, Sugasawa, Ghosh and Chaudhuri (2016) <doi:10.5705/ss.202014.0070>. |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: | no |
| Packaged: | 2016-12-03 11:48:13 UTC; shonosuke |
| Repository: | CRAN |
| Date/Publication: | 2016-12-03 13:24:59 |
Estimation of random heteroscedastic nested error regression models
Description
Calculates the maximum likelihood estimates of the model parameters in random heteroscedastic nested error regression models. The empirical Bayes estimates of area-level parameters with random effects are also given.
Usage
RHNERM(y, X, ni, C, maxr=100)
Arguments
y |
N*1 vector of response values. |
X |
N*p matrix containing N*1 vector of 1 in the first column and vectors of covariates in the rest of columns. |
ni |
m*1 vector of sample sizes in each area. |
C |
m*p matrix of area-level covariates included in the area-level parameters. |
maxr |
maximum number of iteration for computing the maximum likelihood estimates. |
Value
The function returns a list with the following objects:
MLE |
(p+3)*1 vector of maximum likelihood estimates of the model parameters. |
EB |
m*1 vector of empirical Bayes estimates of the area-level parameters. |
Author(s)
Shonosuke Sugasawa
References
Kubokawa, K., Sugasawa, S., Ghosh, M. and Chaudhuri, S. (2016). Prediction in Heteroscedastic nested error regression models with random dispersions. Statistica Sinica, 26, 465-492.
Examples
#generate data
set.seed(1234)
beta=c(1,1); la=1; tau=c(8,4)
m=20; ni=rep(3,m); N=sum(ni)
X=cbind(rep(1,N),rnorm(N))
mu=beta[1]+beta[2]*X[,2]
sig=1/rgamma(m,tau[1]/2,tau[2]/2); v=rnorm(m,0,sqrt(la*sig))
y=c()
cum=c(0,cumsum(ni))
for(i in 1:m){
term=(cum[i]+1):cum[i+1]
y[term]=mu[term]+v[i]+rnorm(ni[i],0,sqrt(sig[i]))
}
#fit the random heteroscedastic nested error regression
C=cbind(rep(1,m),rnorm(m))
fit=RHNERM(y,X,ni,C)
fit
Conditional mean squared error estimation of the empirical Bayes estimators under random heteroscedastic nested error regression models
Description
Calculates the conditional mean squared error estimates of the empirical Bayes estimators under random heteroscedastic nested error regression models based on the parametric bootstrap.
Usage
cmseRHNERM(y, X, ni, C, k=1, maxr=100, B=100)
Arguments
y |
N*1 vector of response values. |
X |
N*p matrix containing N*1 vector of 1 in the first column and vectors of covariates in the rest of columns. |
ni |
m*1 vector of sample sizes in each area. |
C |
m*p matrix of area-level covariates included in the area-level parameters. |
k |
area number in which the conditional mean squared error estimator is calculated. |
maxr |
maximum number of iteration for computing the maximum likelihood estimates. |
B |
number of bootstrap replicates. |
Value
conditional mean squared error estimate in the kth area.
Author(s)
Shonosuke Sugasawa
References
Kubokawa, K., Sugasawa, S., Ghosh, M. and Chaudhuri, S. (2016). Prediction in Heteroscedastic nested error regression models with random dispersions. Statistica Sinica, 26, 465-492.
Examples
#generate data
set.seed(1234)
beta=c(1,1); la=1; tau=c(8,4)
m=20; ni=rep(3,m); N=sum(ni)
X=cbind(rep(1,N),rnorm(N))
mu=beta[1]+beta[2]*X[,2]
sig=1/rgamma(m,tau[1]/2,tau[2]/2); v=rnorm(m,0,sqrt(la*sig))
y=c()
cum=c(0,cumsum(ni))
for(i in 1:m){
term=(cum[i]+1):cum[i+1]
y[term]=mu[term]+v[i]+rnorm(ni[i],0,sqrt(sig[i]))
}
#fit the random heteroscedastic nested error regression
C=cbind(rep(1,m),rnorm(m))
cmse=cmseRHNERM(y,X,ni,C,B=10)
cmse
Mean squared error estimation of the empirical Bayes estimators under random heteroscedastic nested error regression models
Description
Calculates the mean squared error estimates of the empirical Bayes estimators under random heteroscedastic nested error regression models based on the parametric bootstrap.
Usage
mseRHNERM(y, X, ni, C, maxr=100, B=100)
Arguments
y |
N*1 vector of response values. |
X |
N*p matrix containing N*1 vector of 1 in the first column and vectors of covariates in the rest of columns. |
ni |
m*1 vector of sample sizes in each area. |
C |
m*p matrix of area-level covariates included in the area-level parameters. |
maxr |
maximum number of iteration for computing the maximum likelihood estimates. |
B |
number of bootstrap replicates. |
Value
m*1 vector of mean squared error estimates.
Author(s)
Shonosuke Sugasawa
References
Kubokawa, K., Sugasawa, S., Ghosh, M. and Chaudhuri, S. (2016). Prediction in Heteroscedastic nested error regression models with random dispersions. Statistica Sinica, 26, 465-492.
Examples
#generate data
set.seed(1234)
beta=c(1,1); la=1; tau=c(8,4)
m=20; ni=rep(3,m); N=sum(ni)
X=cbind(rep(1,N),rnorm(N))
mu=beta[1]+beta[2]*X[,2]
sig=1/rgamma(m,tau[1]/2,tau[2]/2); v=rnorm(m,0,sqrt(la*sig))
y=c()
cum=c(0,cumsum(ni))
for(i in 1:m){
term=(cum[i]+1):cum[i+1]
y[term]=mu[term]+v[i]+rnorm(ni[i],0,sqrt(sig[i]))
}
#fit the random heteroscedastic nested error regression
C=cbind(rep(1,m),rnorm(m))
mse=mseRHNERM(y,X,ni,C,B=10)
mse