Nothing
## ----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.
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