Prints Logic Regression Output
Description
Prints formulas for objects fitted by logreg.
Usage
## S3 method for class 'logreg'
print(x, nms, notnms, pstyle, ...)
Arguments
| x | object of class logreg, typically the result of the function logreg. |
| nms | names of variables. If nms is provided variable names will be printed, otherwise x$binnames will be used. If that does not exist indices will be used. |
| notnms | names of complements of the variables. If notnms is not provided “not” will be added before the variable names. |
| pstyle | parenthesis style. If pstyle = 1 (the default) rules are more compact than if pstyle = 2. |
| ... | other options are ignored |
Value
If x$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.
Author(s)
Ingo Ruczinski and Charles Kooperberg
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.
See Also
logreg, print.logregmodel, print.logregtree, logreg.testdat
Examples
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)
© 2025 Fred Hutchinson Cancer Center, a 501(c)(3) nonprofit organization.