107 lines
3.2 KiB
R
107 lines
3.2 KiB
R
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#
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# This is a set of tests, with printout, that tries
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# to exercise all of the splitting rules
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# It's less formal than the others in this directory, but covers
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# more.
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# For all of the cross-validations I set xgroups explicitly, so as
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# to avoid any changes in random number allocation of the groups.
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library(rpart)
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require(survival)
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options(digits=4) #avoid trivial rounding changes across R versions
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set.seed(10)
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#
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#
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# Survival, using the stagec data
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#
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# Time to progression in years
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# status 1=progressed, 0= censored
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# age
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# early endocrine therapy 1=no 2=yes
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# % of cells in g2 phase, from flow cytometry
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# tumor grade (Farrow) 1,2,3,4
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# Gleason score (competing grading system)
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# ploidy
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xgroup <- rep(1:10, length=nrow(stagec))
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fit1 <- rpart(Surv(pgtime, pgstat) ~ age + eet + g2+grade+gleason +ploidy,
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stagec, method="poisson",
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control=rpart.control(usesurrogate=0, cp=0, xval=xgroup))
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fit1
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summary(fit1)
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fit2 <- rpart(Surv(pgtime, pgstat) ~ age + eet + g2+grade+gleason +ploidy,
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stagec, xval=xgroup)
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fit2
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summary(fit2)
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#
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# Continuous y variable
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# Use deterministic xgroups: the first group is the 1st, 11th, 21st, etc
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# smallest obs, the second is 2, 12, 22, ...
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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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xvals <- 1:nrow(mystate)
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xvals[order(mystate$income)] <- rep(1:10, length=nrow(mystate))
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fit4 <- rpart(income ~ population + region + illiteracy +life + murder +
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hs.grad + frost , mystate,
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control=rpart.control(minsplit=10, xval=xvals))
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summary(fit4)
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#
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# Check out xpred.rpart
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#
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meany <- mean(mystate$income)
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xpr <- xpred.rpart(fit4, xval=xvals)
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xpr2 <- (xpr - mystate$income)^2
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risk0 <- mean((mystate$income - meany)^2)
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xpmean <- as.vector(apply(xpr2, 2, mean)) #kill the names
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all.equal(xpmean/risk0, as.vector(fit4$cptable[,'xerror']))
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xpstd <- as.vector(apply((sweep(xpr2, 2, xpmean))^2, 2, sum))
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xpstd <- sqrt(xpstd)/(50*risk0)
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all.equal(xpstd, as.vector(fit4$cptable[,'xstd']))
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#
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# recreate subset #3 of the xval
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#
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tfit4 <- rpart(income ~ population + region + illiteracy +life + murder +
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hs.grad + frost , mystate, subset=(xvals!=3),
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control=rpart.control(minsplit=10, xval=0))
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tpred <- predict(tfit4, mystate[xvals==3,])
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all.equal(tpred, xpr[xvals==3,ncol(xpr)])
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# How much does this differ from the "real" formula, more complex,
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# found on page 309 of Breiman et al. ?
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#xtemp <- (xpr2/outer(rep(1,50),xpmean)) - ((mystate$income - meany)^2)/risk0
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#real.se<- xpmean* sqrt(apply(xtemp^2,2,sum))/(risk0*50)
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# Simple yes/no classification model
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fit5 <- rpart(factor(pgstat) ~ age + eet + g2+grade+gleason +ploidy,
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stagec, xval=xgroup)
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fit5
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fit6 <- rpart(factor(pgstat) ~ age + eet + g2+grade+gleason +ploidy,
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stagec, parm=list(prior=c(.5,.5)), xval=xgroup)
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summary(fit6)
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#
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# Fit a classification model to the car data.
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# Now, since Reliability is an ordered category, this model doesn't
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# make a lot of statistical sense, but it does test out some
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# areas of the code that nothing else does
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#
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xcar <- rep(1:8, length=nrow(cu.summary))
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carfit <- rpart(Reliability ~ Price + Country + Mileage + Type,
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method='class', data=cu.summary, xval=xcar)
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summary(carfit)
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