Description Usage Arguments Details Value Functions Examples
These functions provide simple backward elimination/forward selection procedures for logistf models.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 | backward(object, ...)
## S3 method for class 'logistf'
backward(
object,
scope,
steps = 1000,
slstay = 0.05,
trace = TRUE,
printwork = FALSE,
full.penalty = FALSE,
...
)
## S3 method for class 'flic'
backward(
object,
scope,
steps = 1000,
slstay = 0.05,
trace = TRUE,
printwork = FALSE,
full.penalty = FALSE,
...
)
forward(object, ...)
## S3 method for class 'logistf'
forward(
object,
scope,
steps = 1000,
slentry = 0.05,
trace = TRUE,
printwork = FALSE,
pl = TRUE,
...
)
|
object |
A fitted logistf model object. To start with an empty model, create a model fit with a formula= y~1, pl=FALSE. (Replace y by your response variable.) |
... |
Further arguments to be passed to methods. |
scope |
The scope of variables to add/drop from the model. Can be missing for backward, backward will use the terms of the object fit. Alternatively, an arbitrary vector of variable names can be given, to allow that only some of the variables will be competitively selected or dropped. Has to be provided for forward. |
steps |
The number of forward selection/backward elimination steps. |
slstay |
For |
trace |
If |
printwork |
If |
full.penalty |
If |
slentry |
For |
pl |
For forward, computes profile likelihood confidence intervals for the final model if |
The variable selection is simply performed by repeatedly calling add1 or drop1 methods for logistf, and is based on penalized likelihood ratio test. It can also properly handle variables that were defined as factors in the original data set.
An updated logistf, flic
or flac
fit with the finally selected model.
forward
: Forward Selection
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