---
title: "<center> ssgraph with simple example <center>"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{ssgraph with simple sxample}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set( collapse = TRUE, comment = ">", fig.width = 7, fig.height = 7, fig.align = "center" )
```

The `R` package **ssgraph** is designed for Bayesian structure learning in graphical models using spike-and-slab priors. To speed up the computations, the computationally intensive tasks of the package are implemented in `C++` in parallel using **OpenMP**. 

Install **ssgraph** using
```{r eval = FALSE}
install.packages( "ssgraph" )
```

First, we install **ssgraph**
```{r loadpkg, message = FALSE, warning = FALSE}
library( ssgraph )
```

# Example

This is a simple example to see the performance of the package for the Gaussian graphical models. First, by using the function `bdgraph.sim()`, we simulate 100 observations (n = 100) from a multivariate Gaussian distribution with 8 variables (p = 8) and “scale-free” graph structure, as follows:

```{r fig.align = 'center'}
set.seed( 10 )

data.sim <- bdgraph.sim( n = 100, p = 8, graph = "scale-free", vis = TRUE )

round( head( data.sim $ data, 4 ), 2 )
```

Since the generated data are Gaussian, we run `ssgraph` function by choosing `method = "ggm"`, as follows:

```{r fig.align = 'center'}
ssgraph.obj <- ssgraph( data = data.sim, method = "ggm", iter = 5000, 
                        save = TRUE, verbose = FALSE )

summary( ssgraph.obj )
```

To compare the result with true graph
```{r fig.align = 'center'}
compare( data.sim, ssgraph.obj, main = c( "Target", "ssgraph" ), vis = TRUE )
```

```{r fig.align = 'center'}
plotroc( ssgraph.obj, data.sim, cut = 200 )
```