# NS_Multinomial: Calculate network statistic and covariance matrix. In CCMnet: Simulate Congruence Class Model for Networks

## Description

Calculate network statistic and covariance matrix, which is based on a multinomial distribution. Each unit (either node or edge) in the network is assumed to be sampled from a multinomial distribution based on probabilities associated with the network statistic.

## Usage

 ```1 2 3 4 5 6``` ```NS_Multinomial(g, Network_stats, mean_inflate = 0, var_inflate = 1, covPattern = NULL ) ```

## Arguments

 `g` a network object. `Network_stats` Either 'DegreeDist' or 'DegMixing'. `mean_inflate` Add small amount to remove zero values from degree mixing matrix entries. `var_inflate` Multiply the variance by a constant. Used to avoid signular covariance matrices. `covPattern` Currently not used.

## Value

A list of length 2 containing:

 `Network Statistic` Network statistic of the inputted network. `Covariance` Covariance matrix for the network statistic; assumes each unit (either node or edge) is sampled from a multinomial distribution based on probabilities derived from the network statistic.

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15``` ```g = as.network(rgraph(n=500, m=1, tprob=.01, mode='graph', diag=FALSE, replace=FALSE, tielist=NULL, return.as.edgelist=FALSE), directed = FALSE) Prob_Distr_Params=list(NS_Multinomial(g, Network_stats = 'DegreeDist', mean_inflate = .05, var_inflate = 1.05)) Prob_Distr_Params=list(NS_Multinomial(g, Network_stats = 'DegMixing', mean_inflate = .05, var_inflate = 1.05)) ```

CCMnet documentation built on May 29, 2017, 5:41 p.m.