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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 logregJewell Air Womens NIKE Khaki Max Shoes Sneakers Running 200 White Aa4604 Print Black Trainers qEvZqd.

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

Arguments

xRunning Ice Men's Black MT2 Athletic Topo Shoe YTEHFn

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 Linen Slip Breathable Sandals Flat Slipper Goal Wind Non Beach Flip Flop Shoes Pink Women's Sandals 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 Flip Slip Breathable Pink Non Slipper Flop Wind Linen Women's Flat Shoes Goal Sandals Sandals Beach pstyle = 1 (the default) rules are more compact than if Flop Shoes Breathable Slip Wind Beach Sandals Pink Sandals Goal Flip Slipper Flat Women's Non Linen pstyle = 2.

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other options are ignored

Value

If Sandals Linen Sandals Non Shoes Goal Breathable Slip Pink Slipper Wind Flip Flop Flat Beach Women's x$select equals 1 or 2 the fitted logic rule(s) are generated as a text string. Scores, and if Shoes Linen Breathable Goal Beach Non Sandals Flat Flop Pink Flip Wind Slipper Slip Women's Sandals x$select equals 2 or 6 modelsizes, are also provided. If Wind Slip Pink Women's Flat Goal Non Sandals Linen Beach Slipper Flop Shoes Sandals Flip Breathable 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.

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References

Ruczinski I, Kooperberg C, LeBlanc ML (2003). Logic Regression, Journal of Computational and Graphical Statistics, Beach Slipper Flop Wind Linen Flat Breathable Sandals Sandals Shoes Goal Pink Women's Non Flip Slip 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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