# biased-dot-bn-dot-fit: Random network with a biased degree distribution In ddgraph: Distinguish direct and indirect interactions with Graphical Modelling

## Description

A version of random.bn.fit which generates a graph based on degree distribution and beta distribution for probabilities

## Usage

 `1` ```biased.bn.fit(nodes, beta.est, in.degree.distr, bn.graph) ```

## Arguments

 `nodes` character vector of node names `beta.est` the beta distribution parameters for different degrees of a node. Should be a list where [] corresponds to 2-dimenstional contingency table (i.e. one parent, one output). It contains a data.frame with columns shape1, shape2 for the beta distribution, and rows are degrees of freedom (in this case 2, when P(Out=0|Parent=0) and P(Out=0|Parent=1)) `in.degree.distr` a vector with degree distribution for all the nodes in the network (names are ignored, and degree is randomly sampled from this vector) `bn.graph` if the graph structure is already available, then the graph structure in object of class "bn"

## Value

a list of two elements: `bn` - a `bn` object which contains the structure and `bn.fit` - a `bn.fit` object with filled in conditional probabilities

## Examples

 ```1 2 3 4 5 6``` ```# nodes, conditional probability distribution, an indegree distribution nodes = letters[1:5] beta.est = list(data.frame(shape1=2,shape2=3), data.frame(shape1=c(2,4), shape2=c(5,2)), data.frame(shape1=c(1,2,3,4), shape2=c(3,2,1,2))) in.degree.distr = c(0, 1, 1, 2, 2) # make a random graph using these parameters biased.bn.fit(nodes, beta.est, in.degree.distr) ```

ddgraph documentation built on Nov. 17, 2017, 10:50 a.m.