README.Rmd

---
output: github_document
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%"
)
```

# saens

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## Author

Ridson Al Farizal P, Azka Ubaidillah

## Maintainer

Ridson Al Farizal P [ridsonap\@bps.go.id](mailto:ridsonap@bps.go.id)

## Description

Implementation of small area estimation (Fay-Herriot model) with EBLUP (Empirical Best Linear Unbiased Prediction) Approach for non-sampled area estimation by adding cluster information and assuming that there are similarities among particular areas. See also Rao & Molina (2015, ISBN:978-1-118-73578-7) and Anisa et al. (2013) <doi:10.9790/5728-10121519>.

## Installation

You can install the development version of saens from [GitHub](https://github.com/) with:

```{r, eval=FALSE}
# install.packages("devtools")
devtools::install_github("Alfrzlp/saens")
```

or you can install cran version

```{r, eval=FALSE}
install.packages("saens")
```

# Example

This is a basic example which shows you how to solve a common problem:

```{r example, message=FALSE, warning=FALSE}
library(saens)
library(dplyr)
library(tidyr)
library(ggplot2)

windowsFonts(
  poppins = windowsFont('poppins')
)
```

## Data

```{r data}
milk$var <- milk$SD^2

# glimpse(mys)
# glimpse(milk)
```

# EBLUP Model

```{r}
model1 <- eblupfh(yi ~ as.factor(MajorArea), data = milk, vardir = "var")
```

### AIC, BIC, and Loglikehood

```{r}
AIC(model1)
BIC(model1)
logLik(model1)
```

### Coef

```{r}
coef(model1)
```

### Summary

```{r}
summary(model1)
```

### Autoplot

```{r, dpi=500, fig.height=2, fig.width=6}
saens::autoplot(model1, variable = 'MSE')
```

```{r, dpi=500, fig.height=2, fig.width=6}
saens::autoplot(model1, variable = 'RRMSE')
```

```{r, dpi=500, fig.height=2, fig.width=6}
saens::autoplot(model1, variable = 'estimation')
```

# EBLUP Model for Non-sampled Areas

```{r}
model_ns <- eblupfh_cluster(y ~ x1 + x2 + x3, data = mys, vardir = "var", cluster = "clust")
```

### Comparison of estimates and RSE

```{r}
mys$eblup_est <- model_ns$df_res$eblup
mys$eblup_rse <- model_ns$df_res$rse
  
glimpse(mys)
```

```{r, dpi=500, fig.height=3, fig.width=6}
mys %>% 
  select(area, rse, eblup_rse) %>% 
  pivot_longer(-1, names_to = "metode", values_to = "rse") %>%
  ggplot(aes(x = area, y = rse, col = metode)) +
  geom_line() +
    scale_color_discrete(
    labels = c('EBLUP', 'Direct Estimate')
  ) +
  labs(col = NULL, y = 'Estimate', x = 'Domain', title = 'Comparison of RSE') +
  theme(
    legend.position = 'bottom',
    text = element_text(family = 'poppins'),
    axis.ticks.x = element_blank(),
    plot.title = element_text(face = 2, vjust = 0),
    plot.subtitle = element_text(colour = 'gray30', vjust = 0)
  )
```

```{r, dpi=500, fig.height=3, fig.width=6}
mys %>% 
  select(area, y, eblup_est) %>% 
  pivot_longer(-1, names_to = "metode", values_to = "rse") %>%
  ggplot(aes(x = area, y = rse, col = metode)) +
  geom_line() +
    scale_color_discrete(
    labels = c('EBLUP', 'Direct Estimate')
  ) +
  labs(col = NULL, y = 'Estimate', x = 'Domain', title = 'Comparison of Estimates') +
  theme(
    legend.position = 'bottom',
    text = element_text(family = 'poppins'),
    axis.ticks.x = element_blank(),
    plot.title = element_text(face = 2, vjust = 0),
    plot.subtitle = element_text(colour = 'gray30', vjust = 0)
  )
```

## References

-   Rao, J. N., & Molina, I. (2015). Small area estimation. John Wiley & Sons.
-   Anisa, R., Kurnia, A., & Indahwati, I. (2013). Cluster information of non-sampled area in small area estimation. E-Prosiding Internasional\| Departemen Statistika FMIPA Universitas Padjadjaran, 1(1), 69-76.