print.logreg {LogicReg} | R Documentation |
Prints formulas for objects fitted by logreg
.
## S3 method for class 'logreg': print(x, nms, notnms, pstyle, ...)
x |
object of class logreg , typically the result of the
function logreg . |
nms |
names of variables. If nms is provided variable names will
be printted, otherwise x$binnames will be used. If that does
not exist indices will be used. |
notnms |
names of complements of the variables. If
notnms is not provided ``not'' will be added before the
variable names. |
pstyle |
parenthesis style. If pstyle = 1 (the default)
rules are more compact than if pstyle = 2 . |
... |
other options are ignored |
If x$select
equals 1 or 2 the fitted logic rule(s)
are generated as a text string. Scores, and if
x$select
equals 2 or 6 modelsizes, are also provided.
If
x$select
equals 4 or 5 a summary of the permutation test(s) is printed.
If
x$select
equals 3 a summary of the cross validation is printed.
If x$select
is equal to 7 an error message is generated.
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.
logreg
,
print.logregmodel
,
print.logregtree
,
logreg.testdat
data(logreg.savefit1,logreg.savefit2,logreg.savefit3,logreg.savefit4, logreg.savefit5,logreg.savefit6) # # fit a single model # myanneal <- logreg.anneal.control(start = -1, end = -4, iter = 25000, update = 1000) # logreg.savefit1 <- logreg(resp = logreg.testdat[,1], bin=logreg.testdat[, 2:21], # type = 2, select = 1, ntrees = 2, anneal.control = myanneal) # the best score should be in the 0.96-0.98 range print(logreg.savefit1) # # fit multiple models # myanneal2 <- logreg.anneal.control(start = -1, end = -4, iter = 25000, update = 0) # logreg.savefit2 <- logreg(select = 2, ntrees = c(1,2), nleaves =c(1,7), # oldfit = logreg.savefit1, anneal.control = myanneal2) print(logreg.savefit2) # After an initial steep decline, the scores only get slightly better # for models with more than four leaves and two trees. # # cross validation # logreg.savefit3 <- logreg(select = 3, oldfit = logreg.savefit2) print(logreg.savefit3) # 4 leaves, 2 trees should give the best test set score # # null model test # logreg.savefit4 <- logreg(select = 4, anneal.control = myanneal2, oldfit = logreg.savefit1) print(logreg.savefit4) # A summary of the permutation test # # Permutation tests # logreg.savefit5 <- logreg(select = 5, oldfit = logreg.savefit2) print(logreg.savefit5) # A table summarizing the permutation tests # # a greedy sequence # logreg.savefit6 <- logreg(select = 6, ntrees = 2, nleaves =c(1,12), oldfit = logreg.savefit1) print(logreg.savefit6)