---
title: "<center> BDgraph with Simple Examples <center>"
author: "Reza Mohammadi (https://orcid.org/0000-0001-9538-0648)"
date: "`r Sys.Date()`"
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```{r opts, echo = FALSE, message = FALSE, warning = FALSE}
knitr::opts_chunk $ set( collapse = TRUE, comment = " ", fig.width = 7, fig.height = 7, fig.align = "center" )
```

The `R` package **BDgraph** provides statistical tools for Bayesian structure learning for undirected graphical models with *continuous*, *count*, *binary*, and *mixed data*. The package is implemented the recent improvements in the Bayesian graphical models' literature, including [Mohammadi and Wit (2015)](https://projecteuclid.org/euclid.ba/1422468425), [Mohammadi et al. (2023)](https://doi.org/10.1080/01621459.2021.1996377), [Mohammadi et al. (2017)](https://doi.org/10.1111/rssc.12171), [Dobra and Mohammadi (2018)](https://projecteuclid.org/euclid.aoas/1532743478), [Mohammadi et al. (2023)](https://arxiv.org/abs/2307.00127), and [Vinciotti et al. (2022)](https://doi.org/10.48550/arXiv.2203.10118). Besides, the package contains several functions for simulation and visualization, as well as several multivariate datasets taken from the literature. 

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

First, we load **BDgraph** package
```{r loadpkg, message = FALSE, warning = FALSE}
library( BDgraph )
```

Here are two simple examples to show how to use the functionality of the package.

# Example 1: Gaussian Graphical Models

Here 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 200 observations (n = 200) from a multivariate Gaussian distribution with 15 variables (p = 15) and "scale-free" graph structure, as follows

```{r fig.align = 'center'}
set.seed( 20 )

data.sim = bdgraph.sim( n = 200, p = 15, graph = "scale-free", vis = TRUE )
```

Since the generated data are Gaussian, we run the `bdgraph()` function by choosing `method = "ggm"`, as follows

```{r}
bdgraph.obj = bdgraph( data = data.sim, method = "ggm", iter = 5000, verbose = FALSE )
```

To report confusion matrix with cutoff point 0.5:
```{r fig.align = 'center', fig.width = 3, fig.height = 3}
conf.mat( actual = data.sim, pred = bdgraph.obj, cutoff = 0.5 )

conf.mat.plot( actual = data.sim, pred = bdgraph.obj, cutoff = 0.5 )
```

To compare the result with the true graph

```{r fig.align = 'center'}
compare( data.sim, bdgraph.obj, main = c( "Target", "BDgraph" ), vis = TRUE )
```

Now, as an alternative, we run the `bdgraph.mpl()` function which is based on the GGMs and marginal pseudo-likelihood, as follows

```{r fig.align = 'center', fig.width = 3, fig.height = 3}
bdgraph.mpl.obj = bdgraph.mpl( data = data.sim, method = "ggm", iter = 5000, verbose = FALSE )

conf.mat( actual = data.sim, pred = bdgraph.mpl.obj )
conf.mat.plot( actual = data.sim, pred = bdgraph.mpl.obj )
```

We could compare the results of both algorithms with the true graph as follows

```{r fig.align = 'center'}
compare( list( bdgraph.obj, bdgraph.mpl.obj ), data.sim, 
         main = c( "Target", "BDgraph", "BDgraph.mpl" ), vis = TRUE )
```

To see the performance of the BDMCMC algorithm we could plot the ROC curve as follows

```{r fig.align = 'center'}
plotroc( list( bdgraph.obj, bdgraph.mpl.obj ), data.sim, cut = 200,
         labels = c( "BDgraph", "BDgraph.mpl" ), color = c( "blue", "red" ) )
```

# Example 2: Gaussian Copula Graphical Models

Here is a simple example to see the performance of the package for the mixed data using Gaussian copula graphical models. First, by using the function `bdgraph.sim()`, we simulate 300 observations (n = 300) from mixed data (`type = "mixed"`) with 10 variables (p = 10) and "random" graph structure, as follows

```{r fig.align = 'center'}
set.seed( 2 )

data.sim = bdgraph.sim( n = 300, p = 10, type = "mixed", graph = "random", vis = TRUE )
```

Since the generated data are mixed data, we are using  run the `bdgraph()` function by choosing `method = "gcgm"`, as follows:

```{r}
bdgraph.obj = bdgraph( data = data.sim, method = "gcgm", iter = 5000, verbose = FALSE )
```
To compare the result with the true graph, we could run

```{r fig.align = 'center'}
compare( bdgraph.obj, data.sim, main = c( "Target", "BDgraph" ), vis = TRUE )
```

```{r fig.align = 'center'}
plotroc( bdgraph.obj, data.sim, labels = "BDgraph", color = "blue" )
```


For more examples see [Mohammadi and Wit (2019)](https://www.jstatsoft.org/article/view/v089i03).
