## ----setup, include = FALSE---------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

## ----message=FALSE------------------------------------------------------------
library(networkABC)

## ----cache=TRUE---------------------------------------------------------------
net<-network_gen(100,0.33)

## ----messages=FALSE, fig.width=8, fig.height=8--------------------------------
require(network)
plot(network(net$network))

## -----------------------------------------------------------------------------
f<-function(a){
  a<-a[!is.nan(a)]
}

## ----cache=TRUE---------------------------------------------------------------
set.seed(1234)
clco<-rep(0,500)
for(i in 1:500){
  N<-network_gen(500,.33)$net
  N<-N+t(N)
  clco[i]<-mean(f(abs(networkABC::clusteringCoefficient(N))))
}

## -----------------------------------------------------------------------------
mean(clco)

## -----------------------------------------------------------------------------
sd(clco)

## ----message=FALSE, fig.width=8, fig.height=8---------------------------------
ggplot2::qplot(clco)

## ----cache=TRUE---------------------------------------------------------------
set.seed(123)
M<-matrix(rnorm(30),10,3)
result<-abc(data=M)

## ----fig.width=8, fig.height=8------------------------------------------------
networkABC::showHp(result)

## ----fig.width=8, fig.height=8------------------------------------------------
showNp(result)

## ----fig.width=8, fig.height=8------------------------------------------------
showNetwork(result,0.3)

## ----fig.width=8, fig.height=8------------------------------------------------
hist(result$dist)

## ----eval=FALSE---------------------------------------------------------------
# result<-abc(data=M,
#             clust_coeffs=0.33, #you can specify more than one clustering coefficient
#             tolerance=3.5, #maximal distance between simulated and real data
#             # to accept the network
#             number_hubs=3,#the number of hubs
#             iterations=10, #number of iterations
#             number_networks=1000000,#number of network simulated at each iteration
#             hub_probs=NA,#specify the a priori probabilty for each gene to be a hub
#             neighbour_probs=NA,#specify the a priori probability for each couple
#             #of gene to be linked
#             is_probs=1)#set this last option to one.