R: Prints Logic Regression Output

print.logreg {LogicReg} | R Documentation

## 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 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

### 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 ingo@jhu.edu and Charles Kooperberg clk@fredhutch.org.

### 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)

[Package LogicReg version 1.6.2 Index]