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This semester I’m teaching from Hastie, Tibshirani, and Friedman’s book, The Elements of Statistical Learning, 2nd Edition. The authors provide a Mixture Simulation data set that has two continuous predictors and a binary outcome. This data is used to demonstrate classification procedures by plotting classification boundaries in the two predictors. For example, the figure below is a reproduction of Figure 2.5 in the book:

The solid line represents the Bayes decision boundary (i.e., {x: Pr(“orange”|x) = 0.5}), which is computed from the model used to simulate these data. The Bayes decision boundary and other boundaries are determined by one or more surfaces (e.g., Pr(“orange”|x)), which are generally omitted from the graphics. In class, we decided to use the R package `rgl` to create a 3D representation of this surface. Below is the code and graphic (well, a 2D projection) associated with the Bayes decision boundary:

library(rgl) load(url("http://statweb.stanford.edu/~tibs/ElemStatLearn/datasets/ESL.mixture.rda")) dat <- ESL.mixture ## create 3D graphic, rotate to view 2D x1/x2 projection par3d(FOV=1,userMatrix=diag(4)) plot3d(dat$xnew[,1], dat$xnew[,2], dat$prob, type="n", xlab="x1", ylab="x2", zlab="", axes=FALSE, box=TRUE, aspect=1) ## plot points and bounding box x1r <- range(dat$px1) x2r <- range(dat$px2) pts <- plot3d(dat$x[,1], dat$x[,2], 1, type="p", radius=0.5, add=TRUE, col=ifelse(dat$y, "orange", "blue")) lns <- lines3d(x1r[c(1,2,2,1,1)], x2r[c(1,1,2,2,1)], 1) ## draw Bayes (True) decision boundary; provided by authors dat$probm <- with(dat, matrix(prob, length(px1), length(px2))) dat$cls <- with(dat, contourLines(px1, px2, probm, levels=0.5)) pls <- lapply(dat$cls, function(p) lines3d(p$x, p$y, z=1)) ## plot marginal (w.r.t mixture) probability surface and decision plane sfc <- surface3d(dat$px1, dat$px2, dat$prob, alpha=1.0, color="gray", specular="gray") qds <- quads3d(x1r[c(1,2,2,1)], x2r[c(1,1,2,2)], 0.5, alpha=0.4, color="gray", lit=FALSE)

In the above graphic, the probability surface is represented in gray, and the Bayes decision boundary occurs where the plane f(x) = 0.5 (in light gray) intersects with the probability surface.

Of course, the classification task is to estimate a decision boundary given the data. Chapter 5 presents two multidimensional splines approaches, in conjunction with binary logistic regression, to estimate a decision boundary. The upper panel of Figure 5.11 in the book shows the decision boundary associated with additive natural cubic splines in x_{1} and x_{2} (4 df in each direction; 1+(4-1)+(4-1) = 7 parameters), and the lower panel shows the corresponding tensor product splines (4×4 = 16 parameters), which are much more flexible, of course. The code and graphics below reproduce the decision boundaries shown in Figure 5.11, and additionally illustrate the estimated probability surface (note: this code below should only be executed after the above code, since the 3D graphic is modified, rather than created anew):

Reproducing Figure 5.11 (top):

## clear the surface, decision plane, and decision boundary par3d(userMatrix=diag(4)); pop3d(id=sfc); pop3d(id=qds) for(pl in pls) pop3d(id=pl) ## fit additive natural cubic spline model library(splines) ddat <- data.frame(y=dat$y, x1=dat$x[,1], x2=dat$x[,2]) form.add <- y ~ ns(x1, df=3)+ ns(x2, df=3) fit.add <- glm(form.add, data=ddat, family=binomial(link="logit")) ## compute probabilities, plot classification boundary probs.add <- predict(fit.add, type="response", newdata = data.frame(x1=dat$xnew[,1], x2=dat$xnew[,2])) dat$probm.add <- with(dat, matrix(probs.add, length(px1), length(px2))) dat$cls.add <- with(dat, contourLines(px1, px2, probm.add, levels=0.5)) pls <- lapply(dat$cls.add, function(p) lines3d(p$x, p$y, z=1)) ## plot probability surface and decision plane sfc <- surface3d(dat$px1, dat$px2, probs.add, alpha=1.0, color="gray", specular="gray") qds <- quads3d(x1r[c(1,2,2,1)], x2r[c(1,1,2,2)], 0.5, alpha=0.4, color="gray", lit=FALSE)

Reproducing Figure 5.11 (bottom)

## clear the surface, decision plane, and decision boundary par3d(userMatrix=diag(4)); pop3d(id=sfc); pop3d(id=qds) for(pl in pls) pop3d(id=pl) ## fit tensor product natural cubic spline model form.tpr <- y ~ 0 + ns(x1, df=4, intercept=TRUE): ns(x2, df=4, intercept=TRUE) fit.tpr <- glm(form.tpr, data=ddat, family=binomial(link="logit")) ## compute probabilities, plot classification boundary probs.tpr <- predict(fit.tpr, type="response", newdata = data.frame(x1=dat$xnew[,1], x2=dat$xnew[,2])) dat$probm.tpr <- with(dat, matrix(probs.tpr, length(px1), length(px2))) dat$cls.tpr <- with(dat, contourLines(px1, px2, probm.tpr, levels=0.5)) pls <- lapply(dat$cls.tpr, function(p) lines3d(p$x, p$y, z=1)) ## plot probability surface and decision plane sfc <- surface3d(dat$px1, dat$px2, probs.tpr, alpha=1.0, color="gray", specular="gray") qds <- quads3d(x1r[c(1,2,2,1)], x2r[c(1,1,2,2)], 0.5, alpha=0.4, color="gray", lit=FALSE)

Although the graphics above are static, it is possible to embed an interactive 3D version within a web page (e.g., see the `rgl` vignette; best with Google Chrome), using the `rgl` function `writeWebGL`. I gave up on trying to embed such a graphic into this WordPress blog post, but I have created a separate page for the interactive 3D version of Figure 5.11b. Duncan Murdoch’s work with this package is reall nice!

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