# gmodel.P: Generate graphs given a probability matrix In graphon: A Collection of Graphon Estimation Methods

## Description

Given an (n\times n) probability matrix P, gmodel.P generates binary observation graphs corresponding to Bernoulli distribution whose parameter matches to the element of P.

## Usage

 1 gmodel.P(P, rep = 1, noloop = TRUE, symmetric.out = FALSE) 

## Arguments

 P an (n\times n) probability matrix. rep the number of observations to be generated. noloop a logical value; TRUE for graphs without self-loops, FALSE otherwise. symmetric.out a logical value; FALSE for generated graphs to be nonsymmetric, TRUE otherwise. Note that TRUE is supported only if the input matrix P is symmetric.

## Value

depending on rep value, either

(rep=1)

an (n-by-n) observation matrix, or

(rep>1)

a length-rep list where each element is an observation is an (n-by-n) realization from the model.

## Examples

  1 2 3 4 5 6 7 8 9 10 11 ## set inputs modelP <- matrix(runif(16),nrow=4) ## generate 3 observations without self-loops. out <- gmodel.P(modelP,rep=3,noloop=TRUE) ## Visualize generated graphs par(mfrow=c(1,3)) image(out[[1]]) image(out[[2]]) image(out[[3]]) 

graphon documentation built on Sept. 21, 2018, 6:26 p.m.