R: A plot of one Logic Regression tree.

plot.logregtree {LogicReg} | R Documentation

## A plot of one Logic Regression tree.

### Description

Makes a plot of one Logic Regression tree, fitted by `logreg`.

### Usage

    ## S3 method for class 'logregtree'
    plot(x, nms, full=TRUE, and.or.cx=1.0, leaf.sz=1.0,
                    leaf.txt.cx=1.0, coef.cx=1.0, indents=rep(0,4), coef=TRUE,
                    coef.rd=4, ...)

### Arguments

`x` | an object of class `logregtree`, or the `trees` component of such an object. Typically this object will be part of the result of an object of class `logreg`, generated with `select = 1` (single model fit) or `select = 2` (multiple model fit).
`nms` | names of variables. If nms is provided variable names will be plotted, otherwise indices will be used.
`full` | if `TRUE`, the tree occupies the entire window with margins specified by `indents`.
`and.or.cx` | character expansion (size) for the operators and/or.
`leaf.sz` | character expansion for the size of the leaves.
`leaf.txt.cx` | character expansion for the text in the leaves.
`coef.cx` | character expansion for the coefficient string.
`indents` | indents for plot - bottom, left, top, right.
`coef` | if `TRUE`, the coefficient of the tree is plotted.
`coef.rd` | controls how many digits of the above coefficient are displayed.
`...` | graphical parameters can be given as arguments to plot.

### Value

This function makes a plot of one logic tree. The character expansion terms (`and.or.cx, leaf.sz, leaf.txt.cx, coef.cx`) defaults of 1.0 are chosen to generate a pretty plot of a single tree with up to eight leaves (4 levels deep). To plot more than one tree, or trees of different complexity, scale accordingly.

### Author(s)

Ingo Ruczinski ingo@jhu.edu and Charles Kooperberg clk@fredhutch.org.

### References

Ruczinski I, Kooperberg C, LeBlanc ML (2003). Logic Regression, Journal of Computational and Graphical Statistics, 12, 475-511.

Ruczinski I, Kooperberg C, LeBlanc ML (2002). Logic Regression - methods and software. Proceedings of the MSRI workshop on Nonlinear Estimation and Classification (Eds: D. Denison, M. Hansen, C. Holmes, B. Mallick, B. Yu), Springer: New York, 333-344.

Selected chapters from the dissertation of Ingo Ruczinski, available from http://kooperberg.fhcrc.org/logic/documents/ingophd-logic.pdf

### See Also

`logreg`, `frame.logreg`, `logreg.testdat`

### Examples

    data(logreg.savefit2)
    #
    # myanneal2 <- logreg.anneal.control(start = -1, end = -4, iter = 25000, update = 0)
    # logreg.savefit2 <- logreg(resp = logreg.testdat[,1], bin=logreg.testdat[, 2:21],
    #                type = 2, select = 2, ntrees = c(1,2), nleaves =c(1,7),
    #                anneal.control = myanneal2)
    for(i in 1:logreg.savefit2$nmodels) for(j in 1:logreg.savefit2$alltrees[[i]]$ntrees[1]){
       plot.logregtree(logreg.savefit2$alltrees[[i]]$trees[[j]])
       title(main=paste("model",i,"tree",j))
    }

[Package LogicReg version 1.6.2 Index]