Description Value Methods Author(s) Examples
Allows comparison between actual and inferred network.
A vector containing : sensitivity, predictive positive value, the usual Fscore (2*ppv*sens/(sppvpe+sens)), the 1/2 ponderated Fscore ((1+0.5^2)*ppv*sens/(ppv/4+sens)) and the 2 ponderated Fscore ((1+2^2)*ppv*sens/(ppv*4+sens)).
signature(Net = "network", Net_inf = "network", nv = "numeric")
A network object containing the actual network.
A network object containing the inferred network.
A number that indicates at which level of cutoff the comparison should be done.
Bertrand Frederic, Myriam MaumyBertrand.
1 2 3 4 5 6 7 8 9 10 11 12 13  data(Net)
data(Net_inf_PL)
#Comparing true and inferred networks
Crit_values=NULL
#Here are the cutoff level tested
test.seq<seq(0,max(abs(Net_inf_PL@network*0.9)),length.out=200)
for(u in test.seq){
Crit_values<rbind(Crit_values,Patterns::compare(Net,Net_inf_PL,u))
}
matplot(test.seq,Crit_values,type="l",ylab="Criterion value",xlab="Cutoff level",lwd=2)
legend(x="topleft", legend=colnames(Crit_values), lty=1:5,col=1:5,ncol=2,cex=.9)

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