R: Constructs a data frame for one or more Logic Regression...

frame.logreg {LogicReg} | R Documentation

## Constructs a data frame for one or more Logic Regression models

### Description

Evaluates all components of one or more Logic Regression models fitted by a single call to `logreg`.

### Usage

    frame.logreg(fit, msz, ntr, newbin, newresp, newsep, newcens, newweight)

### Arguments

`fit` | object of class `logreg`, that resulted from applying the function `logreg` with `select = 1` (single model fit), `select = 2` (multiple model fit), or `select = 6` (greedy stepwise fit).
`msz` | if `frame.logreg` is executed on an object of class `logreg`, that resulted from applying the function `logreg` with `select = 2` (multiple model fit) or `select = 6` (greedy stepwise fit) all logic trees for all fitted models are returned. To restrict the model size and the number of trees to some models, specify `msz` and `ntr` (for `select = 2`) or just `msz` (for `select = 6`).
`ntr` | see `msz`.
`newbin` | binary predictors to evaluate the logic trees at. If `newbin` is omitted, the original (training) data is used.
`newresp` | the response. If `newbin` is omitted, the original (training) response is used. If `newbin` is specified and `newresp` is omitted, the resulting data frame will not have a response column.
`newsep` | separate (linear) predictors. If `newbin` is omitted, the original (training) predictors are used, even if `newsep` is specified.
`newweight` | case weights. If `newbin` is omitted, the original (training) weights are used. If `newbin` is specified and `newweight` is omitted, the weights are taken to be 1.
`newcens` | censoring indicator. For proportional hazards models and exponential survival models only. If `newbin` is omitted, the original (training) censoring indicators are used. If `newbin` is specified and `newcens` is omitted, the censoring indicators are taken to be 1.

### Details

This function calls `eval.logreg`.

### Value

A data frame. The first column is the response, later columns are weights, censoring indicator, separate predictors (all of which are only provided if they are relevant) and all logic trees. Column names should be transparent.

### 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`, `eval.logreg`, `predict.logreg`, `logreg.testdat`

### Examples

    data(logreg.savefit1,logreg.savefit2,logreg.savefit6)
    #
    # fit a single mode
    # 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)
    frame1 <- frame.logreg(logreg.savefit1)
    #
    # a complete sequence
    # 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)
    frame2 <- frame.logreg(logreg.savefit2)
    #
    # a greedy sequence
    # logreg.savefit6 <- logreg(select = 6, ntrees = 2, nleaves =c(1,12), oldfit = logreg.savefit1)
    frame6  <- frame.logreg(logreg.savefit6, msz = 3:5) # restrict the size

[Package LogicReg version 1.6.2 Index]