| stage.forward {nlnet} | R Documentation |
Nonlinear Forward stagewise regression using DCOL
Description
The subroutine conducts forward stagewise regression using DCOL. Either DCOL roughening or spline roughening is conducted.
Usage
stage.forward(X, y, step.size = 0.01, stop.alpha = 0.01,
stop.var.count = 20, roughening.method = "DCOL", tol = 1e-08,
spline.df = 5, dcol.sel.only = FALSE, do.plot = F)
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. |
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. Once this number is reached, the iteration stops. |
roughening.method |
The method for roughening. The choices are "DCOL" or "spline". |
tol |
The tolerance level of sum of squared changes in the residuals. |
spline.df |
The degree of freedom for the spline. |
dcol.sel.only |
TRUE or FALSE. If FALSE, the selection of predictors will consider both linear and nonlinear association significance. |
do.plot |
Whether to plot the points change in each step. |
Details
Please refer to the reference manuscript for details.
Value
A list object is returned. The components include the following.
found.pred |
The selected predictors (row number). |
ssx.rec |
The magnitude of variance explained using the current predictor at each step. |
$sel.rec |
The selected predictor at each step. |
$p.rec |
The p-value of the association between the current residual and the selected predictor at each step. |
Author(s)
Tianwei Yu<tianwei.yu@emory.edu>
References
https://arxiv.org/abs/1601.05285
See Also
nvsd
Examples
X<-matrix(rnorm(2000),ncol=20)
y<-sin(X[,1])+X[,2]^2+X[,3]
stage.forward(t(X),y,stop.alpha=0.001,step.size=0.05)