hierNet.varimp: Variable importance for hierNet.

In pejovic/hierNet: A Lasso for Hierarchical Interactions

Description

(This is an experimental function.) Calculates a measure of the importance of each variable.

Usage

hierNet.varimp(fit, x, y, ...)

Arguments

fit	The results of a call to the "hierNet"
x	The training set feature matrix used in call produced "fit"
y	The training set response vector used in call produced "fit"
...	additional arguments (not currently used)

Value

Table of variable importance.

Author(s)

Jacob Bien and Robert Tibshirani

References

Bien, J., Taylor, J., Tibshirani, R., (2013) "A Lasso for Hierarchical Interactions." Annals of Statistics. 41(3). 1111-1141.

See Also

hierNet, hierNet.path

Examples

set.seed(12)
x=matrix(rnorm(100*10),ncol=10)
x=scale(x,TRUE,TRUE)
y=x[,1]+2*x[,2]+ x[,1]*x[,2]+3*rnorm(100)
newx=matrix(rnorm(100*10),ncol=10)
fit=hierNet(x,y,lam=50)
yhat=predict.hierNet(fit,newx)

fit=hierNet.path(x,y)
yhat=predict.hierNet(fit,newx)

pejovic/hierNet documentation built on May 25, 2019, 12:45 a.m.