plot.logregtree {LogicReg} | R Documentation |
Makes a plot of one Logic Regression tree, fitted by
logreg
.
## 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, ...)
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. |
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.
Ingo Ruczinski ingo@jhu.edu and Charles Kooperberg clk@fhcrc.org.
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://bear.fhcrc.org/~ingor/logic/documents/myphd-logic.pdf
logreg
,
frame.logreg
,
logreg.testdat
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)) }