plot.selectboost {SelectBoost}

Plot selectboost object

Description

Plot a selectboostboost object.

Usage

## S3 method for class 'selectboost'
plot(
  x,
  verbose = FALSE,
  prop.level = 0.95,
  conf.int.level = 0.95,
  conf.threshold = 0.95,
  ...
)

Arguments

x

Numerical matrix. Result of selectboost (autoboost, fastboost, ...).

verbose

Boolean. Defaults to FALSE.

prop.level

Numeric value. Used to compute the proportion of selection is greater than prop.level. Defaults to .95.

conf.int.level

Numeric value. Confidence level for confidence intervals on estimated proportions of selection. Defaults to .95.

conf.threshold

Numeric value. Used to compute the number of steps (c0) for which the proportion of selection remains greater than conf.threshold. Defaults to .95.

...

. Passed to the plotting functions.

Details

plot.selectboost returns an invisible list and creates four graphics. Two plots the proportion of selection with respect to c0 (by step or according to real scale). On the third graph, no bar means a proportion of selection less than prop.level. Confidence intervals are computed at the conf.int.level level. Barplot of the confidence index (1-min(c0, such that proportion|c0>conf.threshold)).

Value

An invisible list.

Author(s)

Frederic Bertrand, [e-mail]

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

Other Selectboost analyse functions: force.non.inc(), summary.selectboost()

Examples

set.seed(314)
xran=matrix(rnorm(75),15,5)
ybin=sample(0:1,15,replace=TRUE)
yran=rnorm(15)
layout(matrix(1:4,2,2))

data(autoboost.res.x)
plot(autoboost.res.x)

data(autoboost.res.x2)
plot(autoboost.res.x2)