| nvsd {nlnet} | R Documentation |
Nonlinear Variable Selection based on DCOL
Description
This is a nonlinear variable selection procedure for generalized additive models. It's based on DCOL, using forward stagewise selection. In addition, a cross-validation is conducted to tune the stopping alpha level and finalize the variable selection.
Usage
nvsd(X, y, fold = 10, step.size = 0.01, stop.alpha = 0.05, stop.var.count = 20,
max.model.var.count = 10, roughening.method = "DCOL", do.plot = F, pred.method = "MARS")
Arguments
X |
The predictor matrix. Each row is a gene (predictor), each column is a sample. Notice the dimensionality is different than most other packages, where each column is a predictor. This is to conform to other functions in this package that handles gene expression type of data. |
y |
The numerical outcome vector. |
fold |
The fold of cross-validation. |
step.size |
The step size of the roughening process. |
stop.alpha |
The alpha level (significance of the current selected predictor) to stop the iterations. |
stop.var.count |
The maximum number of predictors to select in the forward stagewise selection. Once this number is reached, the iteration stops. |
max.model.var.count |
The maximum number of predictors to select. Notice this can be smaller than the stop.var.count. Stop.var.count can be set more liniently, and this parameter controls the final maximum model size. |
roughening.method |
The method for roughening. The choices are "DCOL" or "spline". |
do.plot |
Whether to plot the points change in each step. |
pred.method |
The prediction method for the cross validation variable selection. As forward stagewise procedure doesn't do prediction, a method has to be borrowed from existing packages. The choices include "MARS", "RF", and "SVM". |
Details
Please refer to the reference for details.
Value
A list object is returned. The components include the following.
selected.pred |
The selected predictors (row number). |
all.pred |
The selected predictors by the forward stagewise selection. The $selected.pred is a subset of this. |
Author(s)
Tianwei Yu<tianwei.yu@emory.edu>
References
https://arxiv.org/abs/1601.05285
See Also
stage.forward
Examples
X<-matrix(rnorm(2000),ncol=20)
y<-sin(X[,1])+X[,2]^2+X[,3]
nvsd(t(X),y,stop.alpha=0.001,step.size=0.05)