deal: node – R documentation

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node

Representation of nodes

Description

An important part of a network is the list of nodes. The nodes summarize the local properties of a node, given the parents of the node.

Usage

node (idx,parents,type="discrete",name=paste(idx),
                 levels=2,levelnames=paste(1:levels),position=c(0,0)) 
## S3 method for class 'node'
 print(x,filename=NA,condposterior=TRUE,condprior=TRUE,...) 
## S3 method for class 'node'
 plot(x,cexscale=10,notext=FALSE,...)
nodes(nw)
nodes(nw) <- value

Arguments

`x`	an object of class `node`.
`parents`	a numeric vector with indices of the parents of the node.
`idx`	an integer, which gives the index of the node (the column number of the corresponding data frame).
`type`	a string, which gives the type of the node. Either `"discrete"` (for factors) or `"continuous"` (for numeric).
`name`	a string, which gives the name used when plotting and printing. Defaults to the column name in the data frame.
`levels`	an integer. If `type` is `"discrete"`, this is the number of levels for the discrete variable.
`levelnames`	if `type` is `"discrete"`, this is a vector of strings (same length as `levels`) with the names of the levels. If `type` is `"continuous"`, the argument is ignored.
`position`	a numeric vector with coordinates where the node should appear in the plot. Usually set by `network` and `drawnetwork`.
`nw`	an object of class `network`.
`value`	a list of elements of class `node`.
`filename`	a string or `NA`. If not `NA`, output is printed to a file.
`condprior`	a logical. If `TRUE`, the conditional prior is printed, see `conditional`.
`condposterior`	a logical. If `TRUE`, the conditional posterior is printed, see `learn`.
`cexscale`	a numeric. Scale parameter to set the size of the nodes.
`notext`	a logical. If `TRUE`, no text is displayed in the nodes on the plot.
`...`	additional plot arguments.

Details

The operations on a node are typically done when operating on a network, so these functions are not to be called directly.

When a network is created with network, the nodes in the nodelist are created using the node procedure.

Local probability distributions are added as the property prob to each node using prob.node. If the node is continuous, this is a numeric vector with the conditional variance and the conditional regression coefficients arising from a regression on the continuous parents, using data. If the node has discrete parents, prob is a matrix with a row for each configuration of the discrete parents. If the node is discrete, prob is a multiway array which gives the conditional probability distribution for each configuration of the discrete parents. The generated prob can be replaced to match the prior information available.

nodes gives the list of nodes of a network. localprob gives the probability distribution for each node in the network.

Value

The node creator function returns an object of class node, which is a list with the following elements (properties),

`idx`	an integer. A unique index for this node. It MUST correspond to the column index of the variable in the data frame.
`name`	a string. The printed name of the node.
`type`	a string. Either `"continuous"` or `"discrete"`.
`levels`	an integer. If the node is of type `"discrete"`, this integer is the number of levels of the node.
`levelnames`	if `type` is `"discrete"`, this is a vector of strings (same length as `levels`) with the names of the levels. If `type` is `"continuous"`, the node does not have this property.
`parents`	a vector of indices of the parents to this node. It is best to manage this vector using the `insert` function.
`prob`	a numeric vector, matrix or multiway array, giving the initial probability distribution. If the node is discrete, `prob` is a multiway array. If the node is continuous, `prob` is a matrix with one row for each configuration of the discrete parents, reducing to a vector if the node has no discrete parents.
`condprior`	a list, generated by `conditional` giving the parameter priors deduced from `jointprior` using the master prior procedure (see `localmaster`).
`condposterior`	a list, which gives the parameter posteriors obtained from `learnnode`.
`loglik`	a numeric giving the log likelihood contribution for this node, calculated in `learnnode`.
`simprob`	a numeric vector, matrix or multiway array similar to `prob`, added by `makesimprob` and used by `rnetwork`.

Author(s)

Susanne Gammelgaard Bottcher,
Claus Dethlefsen rpackage.deal@gmail.com.

deal

Learning Bayesian Networks with Mixed Variables

v1.2-39

GPL (>= 2)

Authors

Susanne Gammelgaard Bottcher, Claus Dethlefsen.

Initial release

2018-10-20