102 lines
3.9 KiB
Plaintext
102 lines
3.9 KiB
Plaintext
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R Under development (unstable) (2019-04-05 r76323) -- "Unsuffered Consequences"
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Copyright (C) 2019 The R Foundation for Statistical Computing
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Platform: x86_64-pc-linux-gnu (64-bit)
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R is free software and comes with ABSOLUTELY NO WARRANTY.
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You are welcome to redistribute it under certain conditions.
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Type 'license()' or 'licence()' for distribution details.
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R is a collaborative project with many contributors.
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Type 'contributors()' for more information and
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'citation()' on how to cite R or R packages in publications.
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Type 'demo()' for some demos, 'help()' for on-line help, or
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'help.start()' for an HTML browser interface to help.
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Type 'q()' to quit R.
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> #
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> # Simplest weight test: treble the weights
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> #
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> # By using the unshrunken estimates the weights will nearly cancel
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> # out: frame$wt, frame$dev, frame$yval2, and improvement will all
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> # be threefold larger, other things will be the same.
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> # The improvement is the splits matrix, column 3, rows with n>0. Other
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> # rows are surrogate splits.
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> library(rpart)
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> require(survival)
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Loading required package: survival
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> set.seed(10)
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>
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> tempc <- rpart.control(maxsurrogate=0, cp=0, xval=0)
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> fit1 <- rpart(Surv(pgtime, pgstat) ~ age + eet + g2+grade+gleason +ploidy,
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+ stagec, control=tempc,
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+ method='poisson', parms=list(shrink=0))
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> wts <- rep(3, nrow(stagec))
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> fit1b <- rpart(Surv(pgtime, pgstat) ~ age + eet + g2+grade+gleason +ploidy,
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+ stagec, control= tempc, parms=list(shrink=0),
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+ method='poisson', weights=wts)
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> fit1b$frame$wt <- fit1b$frame$wt/3
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> fit1b$frame$dev <- fit1b$frame$dev/3
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> fit1b$frame$yval2[,2] <- fit1b$frame$yval2[,2]/3
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> fit1b$splits[,3] <- fit1b$splits[,3]/3
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> zz <- match(c("call", "variable.importance"), names(fit1))
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> all.equal(fit1[-zz], fit1b[-zz]) #all but the "call" and importance
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[1] TRUE
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> all.equal(fit1b$variable.importance/fit1$variable.importance, rep(3,4),
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+ check.attributes = FALSE)
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[1] TRUE
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>
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> #
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> # Compare a pair of multiply weighted fits
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> # In this one, the lengths of where and y won't match
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> # I have to set minsplit to the smallest possible, because otherwise
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> # the replicated data set will sometimes have enough "n" to split, but
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> # the weighted one won't. Use of CP keeps the degenerate splits
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> # (n=2, several covariates with exactly the same improvement) at bay.
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> # For larger trees, the weighted split will sometimes have fewer
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> # surrogates, because of the "at least two obs" rule.
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> #
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> # Create a reproducable psuedo random order using the logisic attractor
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> pseudo <- double(nrow(stagec))
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> pseudo[1] <- pi/4
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> for (i in 2:nrow(stagec)) pseudo[i] <- 4*pseudo[i-1]*(1 - pseudo[i-1])
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>
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> wts <- rep(1:5, length=nrow(stagec))
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> temp <- rep(1:nrow(stagec), wts) #row replicates
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> xgrp <- rep(1:10, length=146)[order(pseudo)]
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> xgrp2<- rep(xgrp, wts)
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> # Direct: replicate rows in the data set, and use unweighted
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> fit2 <- rpart(Surv(pgtime, pgstat) ~ age + eet + g2+grade+gleason +ploidy,
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+ control=rpart.control(minsplit=2, xval=xgrp2, cp=.025),
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+ data=stagec[temp,], method='poisson')
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>
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> # Weighted
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> fit2b<- rpart(Surv(pgtime, pgstat) ~ age + eet + g2+grade+gleason +ploidy,
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+ control=rpart.control(minsplit=2, xval=xgrp, cp=.025),
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+ data=stagec, method='poisson', weight=wts)
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>
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> all.equal(fit2$frame[-2], fit2b$frame[-2]) # the "n" component won't match
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[1] TRUE
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> all.equal(fit2$cptable, fit2b$cptable)
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[1] TRUE
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> #all.equal(fit2$splits[,-1],fit2b$splits[,-1]) #fails
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> toss <- c(49, 64)
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> all.equal(fit2$splits[-toss,-1],fit2b$splits[-toss,-1]) #ok
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[1] TRUE
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> all.equal(fit2$csplit, fit2b$csplit)
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[1] TRUE
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> # Line 49 is a surrogate split in a group whose 2 smallest ages are
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> # 47 and 48. The weighted fit won't split there because it wants to
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> # send at least 2 obs to the left; the replicate fit thinks that there
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> # are several 47's.
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>
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>
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>
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>
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>
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>
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> proc.time()
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user system elapsed
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0.706 0.053 0.753
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