お試し

• この世の偏微分方程式のこと(こちら)
• ロトカ-ヴォルテラのための関数(こちら)

d<-dist(X)
stree<-spantree(d)
depths<-spandepth(stree)
plot(stree,type="t",label=depths)


sum(stree[[2]])

StatsMST<-function(X,perm=TRUE,tobeshuffled=NULL,Nperm=1000,dist.method="euclid"){
	library(vegan)
	Nnode<-length(X[,1])
	distM<-dist(X,method=dist.method)
	
	# calculate stats
	MST<-spantree(distM)
	
	if(!perm){
		return(list(Nnode=Nnode,distM=distM,MST=MST,L=sum(MST[[2]]),PermL=NULL,perm=perm,Nperm=Nperm,dist.method=dist.method))
	}else{
		if(is.null(tobeshuffled)){
			tobeshuffled<-2:length(X[1,])
		}
		PermL<-rep(0,Nperm)

		for(i in 1:Nperm){
			tmpdistM<-dist(ShuffleForPerm(X,tobeshuffled),method=dist.method)
			tmpMST<-spantree(tmpdistM)
			PermL[i]<-sum(tmpMST[[2]])
		}
		return(list(Nnode=Nnode,distM=distM,MST=MST,L=sum(MST[[2]]),PermL=PermL,perm=perm,Nperm=Nperm,dist.method=dist.method))
	}
}


sMST<-StatsMST(X)

plot.MST.L<-function(mst){
	ylim<-range(c(mst$L,mst$PermL))
	plot(sort(mst$PermL),ylim=ylim)
	abline(h=mst$L)
}

plot.MST.L(sMST)
matplot(X[,1:3],type="l")
plot(as.data.frame(X[,1:3])
plot3d(X[,1:3])