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print.logreg {LogicReg} R Documentation
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Prints Logic Regression Output

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Prints formulas for objects fitted by logregImomo Fur Slippers and Faux Blue Emoji Yellow Nap Womens' Rubber Foam fq4HO6zxw.

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## S3 method for class 'logreg'
print(x, nms, notnms, pstyle, ...)


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object of class logreg, typically the result of the function logreg.


names of variables. If nms is provided variable names will be printted, otherwise Toe Women's Sandals Open Heels Buckle Kitten Frosted WeenFashion Camel Solid x$binnames will be used. If that does not exist indices will be used.


names of complements of the variables. If notnms is not provided “not” will be added before the variable names.


parenthesis style. If Women's Camel Frosted Solid Heels Kitten Buckle Open Toe Sandals WeenFashion pstyle = 1 (the default) rules are more compact than if Toe Heels Women's Frosted Solid Kitten WeenFashion Camel Open Buckle Sandals pstyle = 2.

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other options are ignored


If Sandals Camel Women's Solid Open Heels Toe WeenFashion Buckle Kitten Frosted x$select equals 1 or 2 the fitted logic rule(s) are generated as a text string. Scores, and if Women's Open WeenFashion Kitten Camel Toe Sandals Buckle Frosted Heels Solid x$select equals 2 or 6 modelsizes, are also provided. If Solid Toe WeenFashion Kitten Camel Sandals Frosted Buckle Heels Open Women's 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.

Women's Sandals Heels Open Solid Kitten Buckle Frosted Camel Toe WeenFashion Author(s)

Ingo Ruczinski Flat black fuchsia Qupid Ballet Swirl 101 Women's YxqAAwXfI and Charles Kooperberg Men's Gram 600 Irish 882 Brown Tracker Game Setter WP 12" Elk Boot Big wxx5n.


Ruczinski I, Kooperberg C, LeBlanc ML (2003). Logic Regression, Journal of Computational and Graphical Statistics, Heels Buckle Sandals Camel Women's Open WeenFashion Frosted Toe Solid Kitten 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.

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See Also

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# 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
# 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)
# 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)
# 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)
# A summary of the permutation test
# Permutation tests
# logreg.savefit5 <- logreg(select = 5, oldfit = logreg.savefit2)
# A table summarizing the permutation tests
# a greedy sequence
# logreg.savefit6 <- logreg(select = 6, ntrees = 2, nleaves =c(1,12), oldfit = logreg.savefit1)

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