R: Predicted values Logic Regression

predict.logreg {LogicReg} | R Documentation

## Predicted values Logic Regression

### Description

Computes predicted values for one or more Logic Regression models that were fitted by a single call to `logreg`.

### Usage

    ## S3 method for class 'logreg'
    predict(object, msz, ntr, newbin, newsep, ...)

### Arguments

`object` | 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 `predict.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.
`newsep` | separate (linear) predictors. If `newbin` is omitted, the original (training) predictors are used, even if `newsep` is specified.
`...` | other options are ignored

### Details

This function calls `frame.logreg`.

### Value

If `object$select = 1`, a vector with fitted values, otherwise a data frame with fitted values, where columns correspond to models.

### 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`, `frame.logreg`, `logreg.testdat`

### Examples

    data(logreg.savefit1,logreg.savefit2,logreg.savefit6,logreg.testdat)
    #
    # 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)
    z1 <- predict(logreg.savefit1)
    plot(z1, logreg.testdat[,1]-z1, xlab="fitted values", ylab="residuals")
    # myanneal2 <- logreg.anneal.control(start = -1, end = -4, iter = 25000, update = 0)
    # logreg.savefit2 <- logreg(select = 2, nleaves =c(1,7), oldfit = logreg.savefit1,
    #                anneal.control = myanneal2)
    z2  <- predict(logreg.savefit2)
    # logreg.savefit6 <- logreg(select = 6, ntrees = 2, nleaves =c(1,12), oldfit = logreg.savefit1)
    z6 <- predict(logreg.savefit6, msz = 3:5)

[Package LogicReg version 1.6.2 Index]