Plot trajectories.

trajC0(x, ...)

# S3 method for selectboost
trajC0(
  x,
  summary.selectboost.res,
  lasso.coef.path,
  type.x.axis = "noscale",
  type.graph = "boost",
  threshold.level = NULL,
  ...
)

Arguments

x

Numerical matrix. Selectboost object.

...

. Passed to the plotting functions.

summary.selectboost.res

List. Summary of selectboost object.

lasso.coef.path

List. Result of predict.lars.

type.x.axis

Character value. "scale" or "noscale" for the X axis.

type.graph

Character value. Type of graphs: "bars", "lasso" and "boost".

threshold.level

Numeric value. Threshold for the graphs.

Value

An invisible list.

invisible list.

Details

trajC0 returns an invisible list and creates four graphics.

References

selectBoost: a general algorithm to enhance the performance of variable selection methods in correlated datasets, Frédéric Bertrand, Ismaïl Aouadi, Nicolas Jung, Raphael Carapito, Laurent Vallat, Seiamak Bahram, Myriam Maumy-Bertrand, Bioinformatics, 2020. doi:10.1093/bioinformatics/btaa855

See also

fastboost, autoboost and summary.selectboost

Other Selectboost analyze functions: auto.analyze(), plot.summary.selectboost()

Author

Frederic Bertrand, frederic.bertrand@utt.fr

Examples


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")