An Introduction to Recursive Partitioning Using the RPART Routines ...

R-square
0.0 0.2 0.4 0.6 0.8 1.0
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Apparent
X Relative
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X Relative Error
0.4 0.6 0.8 1.0 1.2
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0 1 2 3 4 5
Number of Splits
0 1 2 3 4 5
Number of Splits
Figure 8: Both plots were obtained using the function rsq.rpart(fit3). The gure
on the left shows that the rst split oers the most information. The gure on the
right suggests that the tree should be pruned to include only 1 or 2 splits.
useful plot is the R 2 versus number of splits. The (1 - apparent error) and (1 -
relative error) show howmuch is gained with additional splits. This plot highlights
the dierences between the R 2 values (gure 9).
Finally, it is possible to look at the residuals from this model, just as with a
regular linear regression t, as shown in the following gure.
> plot(predict(fit3),resid(fit3))
> axis(3,at=fit3$frame$yval[fit3$frame$var==''],
labels=row.names(fit3$frame)[fit3$frame$var==''])
> mtext('leaf number',side=3, line=3)
> abline(h=0)
6.3 Solder data (poisson method)
The solder data frame, as explained in the Splus help le, is a design object with 900
observations, which are the results of an experimentvarying 5 factors relevant to the
wave-soldering procedure for mounting components on printed circuit boards. The
response variable, skips, is a count of how many solder skips appeared to a visual
inspection. The other variables are listed below:
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