---
title: "An Application to HB Rao yu Model On sampel dataset"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{An Application to HB Rao yu Model On sampel dataset}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
```

## Load package and data
```{r setup}
library(saeHB.panel)
data("dataPanel")
```

## Fitting Model
```{r}
area = max(dataPanel[,2])
period = max(dataPanel[,3])
vardir = dataPanel[,4]
result=Panel(ydi~xdi1+xdi2,area=area, period=period, vardir=vardir ,iter.mcmc = 10000,thin=5,burn.in = 1000,data=dataPanel)
```

## Extract mean estimation

### Estimation
```{r}
result$Est
```
### Coefficient Estimation
```{r}
result$coefficient
```
### Random effect variance estimation
```{r}
result$refvar
```

## Extract MSE
```{r}
MSE_HB=result$Est$SD^2
summary(MSE_HB)
```
## Extract RSE
```{r}
RSE_HB=sqrt(MSE_HB)/result$Est$MEAN*100
summary(RSE_HB)
```

## You can compare with direct estimator
```{r}
y_dir=dataPanel[,1]
y_HB=result$Est$MEAN
y=as.data.frame(cbind(y_dir,y_HB))
summary(y)
MSE_dir=dataPanel[,4]
MSE=as.data.frame(cbind(MSE_dir, MSE_HB))
summary(MSE)
RSE_dir=sqrt(MSE_dir)/y_dir*100
RSE=as.data.frame(cbind(MSE_dir, MSE_HB))
summary(RSE)
```
