Building and Customizing HydeNet Plots

Jarrod Dalton and Benjamin Nutter


Visualizing your graphical network will be critical to explaining the relationships between the variables in your model. While the printed output explicitly defines the nature of the relationships, the visual representation usually makes it easier to see how all the pieces fit together and evaluate if all of the important relationships have been assembled correctly.

The HydeNet package utilizes the functionality GraphViz package to quickly illustrate the relationships. HydeNet also provides additional tools to address the inherent differences between random variables, deterministic nodes, decision nodes, and utility nodes. Finally, with HydeNet, tools are available to easily customize the appearance of nodes to suit your needs.

A note on GraphViz

GraphViz, or Graphical Visualization Software, is “a way of representing structural information as diagrams of abstract graphs and networks” [1]. It is open source software that the DiagrammeR package utilizes through available libraries and APIs.

We only cover a very small subset of the capabilities available through the DiagrammeR package. A great many customizations may be accomplished, but it is left to the user to research how to make those customizations beyond the basics provided here.

Basic Graphical Network Plots

To illustrate the graphical representations of HydeNetwork objects, we will use the BlackJack data set that comes as part of the package. BlackJack is a hybrid decision network that simulates the card game of the same name [2]. We won’t construct the network here because the code is fairly lengthy. However, the code to construct the object can be reviewed by typing ?BlackJack into the console.

The key aspects of this network are that it contains

When you load HydeNet, it will define some default settings for each of these nodes, but before we show you those settings, let’s take a look at what the GraphViz would return if we chose not to use anything but its defaults.

data(BlackJack, package="HydeNet")
## plot(BlackJack, useHydeDefaults=FALSE)

The figure above shows every node in the network in a circle with white fill and black text. In a decision network, however, different nodes may be inherently different. It would be helpful to be able to distinguish between random variables, deterministic nodes, decision nodes, and utility nodes. The plot.HydeNetwork method uses the package’s default settings to distinguish these types of nodes and can produce the figure below.

What we will see is:


Customizing the Default Settings

We understand that it’s possible that you won’t find our default settings appealing. That’s okay, we promise that our feelings aren’t hurt. In fact, we’re willing to help!

The default plot settings are governed by a global option. You can view the settings this way:

##       type fillcolor   shape fontcolor  color  style
## 1 variable     white ellipse     black  black filled
## 2   determ     white ellipse    gray70 gray70 filled
## 3 decision   #6BAED6    rect     black  black filled
## 4  utility   #FFFFB2 diamond     black  black filled

Let’s assume that you only want to change the default scheme. That is, you want the representation of each node to depend on its type. We can assign GraphViz attributes to a types by using the HydePlotOptions function. To demonstrate, let’s change the defaults so that:

HydePlotOptions(variable=list(shape = "rect", fillcolor = "#A6DBA0"),
                determ = list(shape = "rect", fillcolor = "#E7D4E8",
                              fontcolor = "#1B7837", linecolor = "#1B7837"),
                decision = list(shape = "triangle", fillcolor = "#1B7837",
                                linecolor = "white"),
                utility = list(shape = "circle", fillcolor = "#762A83", 
                               fontcolor = "white"))
## plot(BlackJack)

And at this point, if you decide that maybe our default color scheme really wasn’t so bad, there’s an easy way to go back, too.


Customizing Individual Nodes

The last thing you might want to do, from time to time, is customize an individual node. For instance, you might want to highlight one particular decision. In our case, let’s focus on the decision to take the first hit. To make this node stand out, let’s make it a purple circle.

To do this, we’ll need to use the customNode function. It’s easiest to do this right within the plot call. We’ll even change the size of the node to really make it pop.

     customNodes = customNode(node_id = "hit1",
                              fillcolor = "purple", shape = "circle",
                       fontcolor = "white", height = "2",

Or if we would rather show a series of plots walking through the three decisions, we might not want to change the node appearance, but only the size.

     customNodes = customNode(node_id = "hit1", height = "2"))
     customNodes = customNode(node_id = "hit2", height = "2"))
     customNodes = customNode(node_id = "hit3", height = "2"))