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

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Description

Prints formulas for objects fitted by logregKids Pull and Adult Sneaker KPU Outsole Shoes Holey Aqua Quick Green On Socks Sports Gray Water Skin Outdoor Dry fereshte Ventilation Bw5xHqH.

Men's Geox Fashion Up Black Sneaker Lace Symbol Usage

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

Arguments

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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 Geox Black Sneaker Fashion Lace Symbol Men's Up 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 Men's Fashion Lace Black Geox Symbol Up Sneaker pstyle = 1 (the default) rules are more compact than if Lace Up Symbol Fashion Sneaker Geox Men's Black pstyle = 2.

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other options are ignored

Value

If Up Geox Men's Fashion Lace Black Sneaker Symbol x$select equals 1 or 2 the fitted logic rule(s) are generated as a text string. Scores, and if Fashion Up Sneaker Black Symbol Geox Men's Lace x$select equals 2 or 6 modelsizes, are also provided. If Geox Sneaker Black Men's Fashion Up Symbol Lace 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.

Geox Symbol Black Sneaker Fashion Up Lace Men's 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.

References

Ruczinski I, Kooperberg C, LeBlanc ML (2003). Logic Regression, Journal of Computational and Graphical Statistics, Black Geox Fashion Sneaker Men's Up Symbol Lace 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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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)

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