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

## Description

Given an (n-by-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-by-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 Nov. 17, 2017, 5:28 a.m.