boost {SelectBoost}

Boost step by step functions

Description

Step by step functions to apply the selectboost algorithm.

Usage

boost.normalize(X, eps = 1e-08)

boost.compcorrs(
  Xnorm,
  corrfunc = "cor",
  verbose = FALSE,
  testvarindic = rep(TRUE, ncol(Xnorm))
)

boost.correlation_sign(Correlation_matrice, verbose = FALSE)

boost.findgroups(Correlation_matrice, group, corr = 1, verbose = FALSE)

boost.Xpass(nrowX, ncolX)

boost.adjust(
  X,
  groups,
  Correlation_sign,
  Xpass = boost.Xpass(nrowX, ncolX),
  verbose = FALSE,
  use.parallel = FALSE,
  ncores = 4
)

boost.random(
  X,
  Xpass,
  vmf.params,
  verbose = FALSE,
  B = 100,
  use.parallel = FALSE,
  ncores = 4
)

boost.apply(
  X,
  cols.simul,
  Y,
  func,
  verbose = FALSE,
  use.parallel = FALSE,
  ncores = 4,
  ...
)

boost.select(Boost.coeffs, eps = 10^(-4), version = "lars", verbose = FALSE)

Arguments

X

Numerical matrix. Matrix of the variables.

eps

Numerical value. Response vector.

Xnorm

Numerical matrix. Needs to be centered and l2 normalized.

corrfunc

Character value or function. The function to compute associations between the variables.

verbose

Boolean. Defaults to FALSE.

testvarindic

Boolean vector. Compute associations for a subset of variables. By default, the scope of the computation is the whole dataset, i.e. rep(TRUE,ncol(Xnorm)).

Correlation_matrice

Numerical matrix.

group

Character value or function. The grouping function.

corr

Numerical value. Thresholding value. Defaults to 1.

nrowX

Numerical value

ncolX

Numerical value.

groups

List. List of groups or communities (compact form).

Correlation_sign

Numerical -1/1 matrix.

Xpass

Numerical value. Transformation matrix. Defaults to boost.Xpass(nrowX,ncolX), with nrowX=nrow(X) and ncolX=ncol(X).

use.parallel

Boolean. Defaults to FALSE.

ncores

Numerical value. Number of cores to use. Defaults to 4.

vmf.params

List. List of the parameters ot the fitted von-Mises distributions.

B

Integer value. Number of resampling.

cols.simul

Numerical value. Transformation matrix.

Y

Numerical vector or factor. Response.

func

Function. Variable selection function.

...

. Additionnal parameters passed to the func function.

Boost.coeffs

Numerical matrix. l2 normed matrix of predictors.

version

Character value. "lars" (no intercept value) or "glmnet" (first coefficient is the intercept value).

Details

boost.normalize returns a numeric matrix whose colun are centered and l2 normalized.

boost.compcorrs returns a correlation like matrix computed using the corrfunc function.

boost.Xpass returns the transformation matrix.

boost.findgroups returns a list of groups or communities found using the group function.

boost.Xpass returns the transformation matrix.

boost.adjust returns the list of the parameters ot the fitted von-Mises distributions.

boost.random returns an array with the resampled datasets.

boost.apply returns a matrix with the coefficients estimated using the resampled datasets.

boost.select returns a vector with the proportion of times each variable was selected.

Value

Various types depending on the function.

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 functions: autoboost(), fastboost(), plot_selectboost_cascade, selectboost_cascade

Examples

set.seed(314)
xran=matrix(rnorm(200),20,10)
yran=rnorm(20)
xran_norm <- boost.normalize(xran)

xran_corr<- boost.compcorrs(xran_norm)

xran_corr_sign <- boost.correlation_sign(xran_corr)

xran_groups <- boost.findgroups(xran_corr, group=group_func_1, .3)
xran_groups_2 <- boost.findgroups(xran_corr, group=group_func_2, .3)

xran_Xpass <- boost.Xpass(nrow(xran_norm),ncol(xran_norm))

xran_adjust <- boost.adjust(xran_norm, xran_groups$groups, xran_corr_sign)

#Not meaningful, should be run with B>=100
xran_random <- boost.random(xran_norm, xran_Xpass, xran_adjust$vmf.params, B=5)


xran_random <- boost.random(xran_norm, xran_Xpass, xran_adjust$vmf.params, B=100)


xran_apply <- boost.apply(xran_norm, xran_random, yran, lasso_msgps_AICc)

xran_select <- boost.select(xran_apply)