The mvpart Package - NexTag Supports Open Source Initiatives

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28 rpart.class rpart.branch Rpart.branch Description Internal function for Rpart Details rpart.branch is to be used only internally. rpart.class Set up for ‘class’ Method for an Rpart Model Description Set up for the class method for an rpart model. Usage rpart.class(y, offset, parms, wt) Arguments y offset parms wt the responses. NULL: anything else is an error. a named list of parameters. case weights. Details rpart.class is to be used only by the function rpart. It validates the input parameter list: if that is missing or NULL default values are supplied. Value Returns a list consisting of y y as factor codes, parms a named list of parameters. This will have components prior, loss and split (and more if supplied). numresp counts the number of responses (one plus the number of classes). a vector of counts for each class. ylevels the response levels corresponding to the codes given in y. summary text a function to be invoked by summary.rpart, a function to be invoked by text.rpart.
part.control 29 See Also rpart rpart.control Control for Rpart Models Description Various parameters that control aspects of the rpart fit. Usage rpart.control(minsplit=5, minbucket=round(minsplit/3), cp=0.01, maxcompete=4, maxsurrogate=0, usesurrogate=2, xval=10, surrogatestyle=0, maxdepth=30, ...) Arguments minsplit minbucket cp maxcompete the minimum number of observations that must exist in a node, in order for a split to be attempted. the minimum number of observations in any terminal node. If only one of minbucket or minsplit is specified, the code either sets minsplit to minbucket*3 or minbucket to minsplit/3, as appropriate. complexity parameter. Any split that does not decrease the overall lack of fit by a factor of cp is not attempted. For instance, with anova splitting, this means that the overall Rsquare must increase by cp at each step. <strong>The</strong> main role of this parameter is to save computing time by pruning off splits that are obviously not worthwhile. Essentially,the user informs the program that any split which does not improve the fit by cp will likely be pruned off by cross-validation, and that hence the program need not pursue it. the number of competitor splits retained in the output. It is useful to know not just which split was chosen, but which variable came in second, third, etc. maxsurrogate the number of surrogate splits retained in the output. If this is set to zero the compute time will be shortened, since approximately half of the computational time (other than setup) is used in the search for surrogate splits. usesurrogate how to use surrogates in the splitting process. 0= display only; an observation with a missing value for the primary split rule is not sent further down the tree. 1= use surrogates, in order, to split subjects missing the primary variable; if all surrogates are missing the observation is not split. 2= if all surrogates are missing, then send the observation in the majority direction. A value of 0 corresponds to the action of tree, and 2 to the recommendations of Breiman, et.al. xval number of cross-validations
Page 1 and 2: The mvpart Package February 16, 200
Page 3 and 4: cmds.diss 3 Usage data(car.test.fra
Page 5 and 6: descendants 5 descendants Descendan
Page 7 and 8: kyphosis 7 Value Should be intercha
Page 9 and 10: makeForm 9 Value collapse logical.
Page 11 and 12: model.frame.rpart 11 ylab y-axis la
Page 13 and 14: na.rpart 13 Value bars legend bord
Page 15 and 16: plot.rpart 15 Value A named (by nod
Page 17 and 18: plotcp 17 plotcp Plot a Complexity
Page 19 and 20: predict.rpart 19 Side Effects a plo
Page 21 and 22: print.rpart 21 print.rpart Print an
Page 23 and 24: prune.rpart 23 Examples data(car.te
Page 25 and 26: part 25 Examples data(solder) fit
Page 27: part.anova 27 plot(z.auto); text(z.
Page 31 and 32: part.exp 31 Value Returns a list co
Page 33 and 34: part.object 33 Value Returns a list
Page 35 and 36: part.pca 35 Arguments tree pts plt.
Page 37 and 38: pconvert 37 rpconvert Update an rpa
Page 39 and 40: snip.rpart 39 Note Common transform
Page 41 and 42: spider 41 Format This data frame co
Page 43 and 44: summary.rpart 43 summary.rpart Summ
Page 45 and 46: trclcomp 45 Side Effects See Also t
Page 47 and 48: xpred.rpart 47 dinf, eps, replace.n
Page 49 and 50: Index ∗Topic datasets car.test.fr

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