hierNet.varimp: Variable importance for hierNet.

In 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(fit,newx)

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

Example output

GG converged in 31 iterations.
i,lam= 1 129.72
GG converged in 1 iterations.
i,lam= 2 101.8
GG converged in 20 iterations.
i,lam= 3 79.89
GG converged in 26 iterations.
i,lam= 4 62.69
GG converged in 26 iterations.
i,lam= 5 49.2
GG converged in 26 iterations.
i,lam= 6 38.61
GG converged in 48 iterations.
i,lam= 7 30.3
GG converged in 57 iterations.
i,lam= 8 23.78
GG converged in 62 iterations.
i,lam= 9 18.66
GG converged in 55 iterations.
i,lam= 10 14.64
GG converged in 63 iterations.
i,lam= 11 11.49
GG converged in 60 iterations.
i,lam= 12 9.02
GG converged in 119 iterations.
i,lam= 13 7.08
GG converged in 122 iterations.
i,lam= 14 5.55
GG converged in 139 iterations.
i,lam= 15 4.36
GG converged in 147 iterations.
i,lam= 16 3.42
GG converged in 200 iterations.
i,lam= 17 2.68
GG converged in 374 iterations.
i,lam= 18 2.11
GG converged in 291 iterations.
i,lam= 19 1.65
GG converged in 602 iterations.
i,lam= 20 1.3
GG converged in 657 iterations.

hierNet documentation built on Feb. 6, 2020, 5:09 p.m.