boost | R Documentation |
Step by step functions to apply the selectboost algorithm.
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)
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 |
testvarindic |
Boolean vector. Compute associations for a subset of variables.
By default, the scope of the computation is the whole dataset, i.e. |
Correlation_matrice |
Numerical matrix. |
group |
Character value or function. The grouping function. |
corr |
Numerical value. Thresholding value. Defaults to |
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 |
use.parallel |
Boolean.
Defaults to |
ncores |
Numerical value. Number of cores to use.
Defaults to |
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 |
Boost.coeffs |
Numerical matrix. l2 normed matrix of predictors. |
version |
Character value. "lars" (no intercept value) or "glmnet" (first coefficient is the intercept value). |
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.
Various types depending on the function.
Frederic Bertrand, frederic.bertrand@utt.fr
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
fastboost
, autoboost
Other Selectboost functions:
autoboost()
,
fastboost()
,
plot_selectboost_cascade
,
selectboost_cascade
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)
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