imp {bclust} | R Documentation |
The function computes the log Bayes factors for the hypothesis H0: the variable or the variable-cluster combination is useful for clustering against H1: the variable or the variable-cluster combination is useless. The Bayes factors are computed for the optimal allocation found by the bclust
function.
imp(x)
x |
A |
var |
A vector being the log Bayes factor of d_{v}=1 against d_{v}=0, see bclust for details. |
varclust |
A vector being the log Bayes factor of g_{vc}=1 against g_{vc}=0, see bclust for details. |
repno |
The number of replicates producing each row of |
labels |
The vector of variable labels extracted from the |
order |
The order of |
data(gaelle) gaelle.id<-rep(1:14,c(3,rep(4,13))) # first 3 rows replication of ColWT, 4 for the rest gaelle.bclust<-bclust(gaelle,rep.id=gaelle.id, transformed.par=c(-1.84,-0.99,1.63,0.08,-0.16,-1.68), var.select=TRUE) gaelle.imp<-imp(gaelle.bclust) #plot the variable importances par(mfrow=c(1,1)) #retreive graphic defaults mycolor<-gaelle.imp$var mycolor<-c() mycolor[gaelle.imp$var>0]<-"black" mycolor[gaelle.imp$var<=0]<-"white" viplot(var=gaelle.imp$var,xlab=gaelle.imp$labels,col=mycolor) #plot important variables with balck viplot(var=gaelle.imp$var,xlab=gaelle.imp$labels, sort=TRUE,col=heat.colors(length(gaelle.imp$var)), xlab.mar=10,ylab.mar=4) mtext(1, text = "Metabolite", line = 7,cex=1.5)# add x axis label mtext(2, text = "Log Bayes Factor", line = 3,cex=1.2)# add y axis labels #sort importnaces and use heat colors add some labels to the x and y axes