R/vec.plot.R

In forestFloor: Visualizes Random Forests with Feature Contributions

#f2 - show vec plot 2D and 3D
vec.plot = function(model,X,i.var,grid.lines=100,VEC.function=mean,zoom=1,limitY=F,col="#20202050") {
  
  #compute grid range
  d = length(i.var)
  scales = lapply(i.var, function(i) {
    rXi = range(X[,i])
    span = abs(rXi[2]-rXi[1])*zoom/2
    center = mean(rXi)
    seq(center-span,center+span,length.out=grid.lines)
  })
  
  #expand grid range to a n-dimensional VEC-space and predict by model
  anchor.points = as.matrix(expand.grid(scales),dimnames=NULL)
  Xgeneralized=apply(X,2,VEC.function)    
  Xtest.vec = data.frame(t(replicate(dim(anchor.points)[1],Xgeneralized)))
  Xtest.vec[,i.var] = anchor.points
  yhat.vec = predict(model,Xtest.vec)
  
  #add observations to VEC-space
  values.to.plot=X[,i.var]
  Xmean=apply(X,2,VEC.function)
  Xtest.obs = data.frame(t(replicate(dim(X)[1],Xgeneralized)))
  Xtest.obs[,i.var] = values.to.plot
  yhat.obs =  predict(model, Xtest.obs)
  
  #plot VEC-space versus predictions (only 2D and 3D plot supported)
  if(d==2) { #if 2D VEC space
    plot3d(x=values.to.plot[,1],y=values.to.plot[,2],z=yhat.obs,
           xlab=names(X)[i.var][1],ylab=names(X)[i.var][2],main="VEC-SURFACE",col=col)
    surface3d(x=scales[[1]],
              y=scales[[2]],
              z=yhat.vec,col="#404080",size=4,alpha=0.4)
  }else{ #otherwise if 1D VEC-space
    if(limitY) {ylim = range(model$y)} else {ylim = NULL}
    plot(x=scales[[1]],y=yhat.vec,col="red",type="l",xlab=names(X)[i.var][1],ylim=ylim)
    points(values.to.plot,yhat.obs)
  }
}