data(autoboost.res.x)
data(diabetes, package="lars")
### With lasso trajectories
m.x<-lars::lars(diabetes$x,diabetes$y)
plot(m.x)
mm.x<-predict(m.x,type="coef",mode="lambda")
autoboost.res.x.mean = summary(autoboost.res.x)
par(mfrow=c(2,2),mar=c(4,4,1,1))
trajC0(autoboost.res.x,autoboost.res.x.mean,lasso.coef.path=mm.x,type.graph="lasso")
trajC0(autoboost.res.x,autoboost.res.x.mean)
trajC0(autoboost.res.x,autoboost.res.x.mean,type.graph="bars")
trajC0(autoboost.res.x,autoboost.res.x.mean,type.x.axis ="scale")
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