144 lines
5.1 KiB
R
144 lines
5.1 KiB
R
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# Any necessary setup
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library(rpart)
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options(na.action="na.omit")
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options(digits=4) # to match earlier output
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set.seed(1234)
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mystate <- data.frame(state.x77, region=factor(state.region))
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names(mystate) <- c("population","income" , "illiteracy","life" ,
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"murder", "hs.grad", "frost", "area", "region")
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#
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# Test out the "user mode" functions, with an anova variant
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#
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# The 'evaluation' function. Called once per node.
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# Produce a label (1 or more elements long) for labeling each node,
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# and a deviance. The latter is
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# - of length 1
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# - equal to 0 if the node is "pure" in some sense (unsplittable)
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# - does not need to be a deviance: any measure that gets larger
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# as the node is less acceptable is fine.
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# - the measure underlies cost-complexity pruning, however
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temp1 <- function(y, wt, parms) {
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wmean <- sum(y*wt)/sum(wt)
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rss <- sum(wt*(y-wmean)^2)
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list(label= wmean, deviance=rss)
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}
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# The split function, where most of the work occurs.
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# Called once per split variable per node.
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# If continuous=T
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# The actual x variable is ordered
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# y is supplied in the sort order of x, with no missings,
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# return two vectors of length (n-1):
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# goodness = goodness of the split, larger numbers are better.
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# 0 = couldn't find any worthwhile split
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# the ith value of goodness evaluates splitting obs 1:i vs (i+1):n
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# direction= -1 = send "y< cutpoint" to the left side of the tree
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# 1 = send "y< cutpoint" to the right
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# this is not a big deal, but making larger "mean y's" move towards
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# the right of the tree, as we do here, seems to make it easier to
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# read
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# If continuos=F, x is a set of integers defining the groups for an
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# unordered predictor. In this case:
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# direction = a vector of length m= "# groups". It asserts that the
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# best split can be found by lining the groups up in this order
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# and going from left to right, so that only m-1 splits need to
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# be evaluated rather than 2^(m-1)
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# goodness = m-1 values, as before.
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#
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# The reason for returning a vector of goodness is that the C routine
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# enforces the "minbucket" constraint. It selects the best return value
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# that is not too close to an edge.
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temp2 <- function(y, wt, x, parms, continuous) {
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# Center y
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n <- length(y)
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y <- y- sum(y*wt)/sum(wt)
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if (continuous) {
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# continuous x variable
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temp <- cumsum(y*wt)[-n]
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left.wt <- cumsum(wt)[-n]
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right.wt <- sum(wt) - left.wt
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lmean <- temp/left.wt
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rmean <- -temp/right.wt
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goodness <- (left.wt*lmean^2 + right.wt*rmean^2)/sum(wt*y^2)
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list(goodness= goodness, direction=sign(lmean))
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}
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else {
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# Categorical X variable
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ux <- sort(unique(x))
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wtsum <- tapply(wt, x, sum)
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ysum <- tapply(y*wt, x, sum)
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means <- ysum/wtsum
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# For anova splits, we can order the categories by their means
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# then use the same code as for a non-categorical
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ord <- order(means)
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n <- length(ord)
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temp <- cumsum(ysum[ord])[-n]
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left.wt <- cumsum(wtsum[ord])[-n]
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right.wt <- sum(wt) - left.wt
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lmean <- temp/left.wt
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rmean <- -temp/right.wt
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list(goodness= (left.wt*lmean^2 + right.wt*rmean^2)/sum(wt*y^2),
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direction = ux[ord])
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}
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}
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# The init function:
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# fix up y to deal with offsets
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# return a dummy parms list
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# numresp is the number of values produced by the eval routine's "label"
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# numy is the number of columns for y
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# summary is a function used to print one line in summary.rpart
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# In general, this function would also check for bad data, see rpart.poisson
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# for instace.
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temp3 <- function(y, offset, parms, wt) {
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if (!is.null(offset)) y <- y-offset
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list(y=y, parms=0, numresp=1, numy=1,
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summary= function(yval, dev, wt, ylevel, digits ) {
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paste(" mean=", format(signif(yval, digits)),
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", MSE=" , format(signif(dev/wt, digits)),
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sep='')
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})
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}
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alist <- list(eval=temp1, split=temp2, init=temp3)
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fit1 <- rpart(income ~population +illiteracy + murder + hs.grad + region,
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mystate, control=rpart.control(minsplit=10, xval=0),
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method=alist)
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fit2 <- rpart(income ~population +illiteracy + murder + hs.grad + region,
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mystate, control=rpart.control(minsplit=10, xval=0),
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method='anova')
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# Other than their call statement, and a longer "functions" component in
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# fit1, fit1 and fit2 should be identical.
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all.equal(fit1$frame, fit2$frame)
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all.equal(fit1$splits, fit2$splits)
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all.equal(fit1$csplit, fit2$csplit)
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all.equal(fit1$where, fit2$where)
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all.equal(fit1$cptable, fit2$cptable)
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# Now try xpred on it
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xvtemp <- rep(1:5, length=50)
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xp1 <- xpred.rpart(fit1, xval=xvtemp)
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xp2 <- xpred.rpart(fit2, xval=xvtemp)
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aeq <- function(x,y) all.equal(as.vector(x), as.vector(y))
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aeq(xp1, xp2)
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fit3 <- rpart(income ~population +illiteracy + murder + hs.grad + region,
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mystate, control=rpart.control(minsplit=10, xval=xvtemp),
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method='anova')
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zz <- apply((mystate$income - xp1)^2,2, sum)
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aeq(zz/fit1$frame$dev[1], fit3$cptable[,4]) #reproduce xerror
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zz2 <- sweep((mystate$income-xp1)^2,2, zz/nrow(xp1))
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zz2 <- sqrt(apply(zz2^2, 2, sum))/ fit1$frame$dev[1]
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aeq(zz2, fit3$cptable[,5]) #reproduce se(xerror)
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