HydeNetwork: Define a Probablistic Graphical Network
In HydeNet: Hybrid Bayesian Networks Using R and JAGS

Description Usage Arguments Details Value Author(s) Examples

Using either a directed acyclic graph (DAG) or a list of models, define a probabilistic graphical network to serve as the basis of building a model.

HydeNetwork(nodes, ...)

## S3 method for class 'formula'
HydeNetwork(nodes, data = NULL, ...)

## S3 method for class 'list'
HydeNetwork(nodes, ...)

`nodes`	Either a formula that defines the network or a list of model objects.
`...`	additional arguments to other methods. Not currently used.
`data`	A data frame with the data for estimating node parameters.

The DAG becomes only one element of the object returned by HydeNetwork. The dag object is used to extract the node names and a list of parents for each node. These will be used to help quantify the relationships.

When given a formula, the relationships are defined, but are not quantified until writeNetworkModel is called.

When a list of models is given, rather than refitting models when writeNetworkModel is called, the quantified relationships are placed into the object.

Returns an object of class HydeNetwork. The object is really just a list with the following components:

nodes a vector of node names
parents a named list with each element being a vector of parents for the node named.
nodeType a named list with each element specifying the JAGS distribution type.
nodeFormula a named list with the formulae specifying the relationships between nodes.
nodeFitter a named list giving the fitting function for each node.
nodeFitterArgs A named list with additional arguments to be passed to fitter functions.
nodeParams A named list. Each element is a vector of parameters that will be expected by JAGS.
fromData A named list with the logical value of whether parameters should be estimated from the data.
nodeData A named list with the data for each node. If a node's entry in fromData is TRUE and nodeData is NULL, it will look to the data attribute instead.
factorLevels If the vector associated with the node is a factor (or character), the levels of the factor are stored here. Although it may seem redundant, it allows factor levels to be specified in cases where the node is not define with data. If data are provided to the node, this element is determined from the data and cannot be manually overwritten.
nodeModel A list of model objects. This is a storing place for models that have already been fit so that they don't have to be refit again.
nodeDecision A named list of logical flags for whether the node is a decision node or not.
nodeUtility A named list of logical flags for whether the node is a utility node or not.
dag The adjacency matrix defining the network. Most of the plotting utilities will be based on this element.
data A common data frame for nodes that do not have their own unique data source.
network_formula The original formula passed to construct the model.

@note These objects can get pretty large. In versions of R earlier than 3.2, it can take a while to print the large network objects if you simply type the object name into the console. It is recommended that you always explicitly invoke the 'print' function (ie, print(Net) instead of just Net) to save yourself some valuable time.

Jarrod Dalton and Benjamin Nutter

#* Formula Input
Net <- HydeNetwork(~ wells + 
                     pe | wells + 
                     d.dimer | pregnant*pe + 
                     angio | pe + 
                     treat | d.dimer*angio + 
                     death | pe*treat,
                     data = PE) 
print(Net)

#* Model Input
g1 <- lm(wells ~ 1, data=PE)
g2 <- glm(pe ~ wells, data=PE, family="binomial")
g3 <- lm(d.dimer ~ pe + pregnant, data=PE)
g4 <- xtabs(~ pregnant, data=PE)
g5 <- glm(angio ~ pe, data=PE, family="binomial")
g6 <- glm(treat ~ d.dimer + angio, data=PE, family="binomial")
g7 <- glm(death ~ pe + treat, data=PE, family="binomial")

bagOfModels <- list(g1,g2,g3,g4,g5,g6,g7)

bagNet <- HydeNetwork(bagOfModels)
print(bagNet)