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 |
nms |
names of variables. If nms is provided variable names will be plotted, otherwise indices will be used. |
full |
if |
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 |
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 https://research.fredhutch.org/content/dam/stripe/kooperberg/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))
}