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)