SelectBoost.FDA is an R package for variable selection
in functional data analysis. It combines FDA-native preprocessing and
design objects with grouped stability selection, interval summaries,
FDA-aware SelectBoost, and a small validation layer for
simulation and benchmarking.
The package is designed for workflows where functional predictors are observed on a grid, represented through basis expansions, or reduced to FPCA scores, and where strong local or block-wise correlation makes ordinary variable selection unstable.
SelectBoost wrappers plus a plain
SelectBoost baseline.F1 comparisons between selectboost_fda() and
plain SelectBoost.You can install the development version from GitHub with:
# install.packages("remotes")
remotes::install_github("bertran7/SelectBoost.FDA")Some workflows rely on optional backends:
glmnet for lasso-based grouped stability
selection.grpreg for group lasso.SGL for sparse-group lasso.FDboost and stabs for the native
FDboost stability-selection route.The package ships with small example datasets so the full workflow can start from raw functional inputs.
data("spectra_example", package = "SelectBoost.FDA")
idx <- 1:30
design <- fda_design(
response = spectra_example$response[idx],
predictors = list(
signal = fda_grid(
spectra_example$predictors$signal[idx, ],
argvals = spectra_example$grid,
name = "signal",
unit = "nm"
),
nuisance = fda_grid(
spectra_example$predictors$nuisance[idx, ],
argvals = spectra_example$grid,
name = "nuisance",
unit = "nm"
)
),
scalar_covariates = spectra_example$scalar_covariates[idx, ],
transforms = list(
signal = fda_fpca(n_components = 3),
nuisance = fda_bspline(df = 5)
),
scalar_transform = fda_standardize(),
family = "gaussian"
)
summary(design)
#> FDA design summary
#> observations: 30
#> features: 10
#> family: gaussian
#> response available: TRUE
#> functional predictors: 2
#> scalar covariates: 2
#> predictor representation n_features
#> nuisance basis 5
#> signal basis 3
#> age scalar 1
#> treatment scalar 1
head(selection_map(design, level = "basis"))
#> predictor representation basis_type
#> nuisance.spline nuisance basis spline
#> signal.fpca signal basis fpca
#> source_representation n_components
#> nuisance.spline grid 5
#> signal.fpca grid 3
#> first_component last_component
#> nuisance.spline B1 B5
#> signal.fpca PC1 PC3
#> components domain_start
#> nuisance.spline B1, B2, B3, B4, B5 1100
#> signal.fpca PC1, PC2, PC3 1100
#> domain_end
#> nuisance.spline 2500
#> signal.fpca 2500SelectBoost.FDA extends SelectBoost with
block-aware and region-aware grouping while keeping the original
perturbation engine.
fit_sb <- fit_selectboost(
design,
mode = "fast",
steps.seq = c(0.6, 0.3),
c0lim = FALSE,
B = 4
)
summary(fit_sb)
#> FDA SelectBoost summary
#> family: gaussian
#> predictors: 4
#> mode: fast
#> features: 10
#> groups: 4
#> c0 values: 2
head(selection_map(fit_sb, level = "group", c0 = colnames(fit_sb$feature_selection)[1]))
#> predictor group_id group representation
#> 1 signal 1 signal basis
#> 2 nuisance 2 nuisance basis
#> 3 age 3 age scalar
#> 4 treatment 4 treatment scalar
#> basis_type source_representation n_features
#> 1 fpca grid 3
#> 2 spline grid 5
#> 3 scalar 1
#> 4 scalar 1
#> start_position end_position start_argval end_argval
#> 1 1 3 PC1 PC3
#> 2 1 5 B1 B5
#> 3 1 1 age age
#> 4 1 1 treatment treatment
#> domain_start domain_end c0 mean_selection
#> 1 1100 2500 c0 = 0.6 0.6666667
#> 2 1100 2500 c0 = 0.6 0.2500000
#> 3 age age c0 = 0.6 0.2500000
#> 4 treatment treatment c0 = 0.6 1.0000000
#> max_selection selected_features
#> 1 1.00 2
#> 2 0.50 4
#> 3 0.25 1
#> 4 1.00 1Grouped stability selection is available through a common FDA
interface. The lasso route below requires the optional
glmnet package.
if (requireNamespace("glmnet", quietly = TRUE)) {
fit_stab <- fit_stability(
design,
selector = "lasso",
B = 8,
cutoff = 0.5,
seed = 1
)
summary(fit_stab)
head(selection_map(fit_stab, level = "group"))
}
#> predictor group_id group representation
#> 1 signal 1 signal basis
#> 2 nuisance 2 nuisance basis
#> 3 age 3 age scalar
#> 4 treatment 4 treatment scalar
#> basis_type source_representation n_features
#> 1 fpca grid 3
#> 2 spline grid 5
#> 3 scalar 1
#> 4 scalar 1
#> start_position end_position start_argval end_argval
#> 1 1 3 PC1 PC3
#> 2 1 5 B1 B5
#> 3 1 1 age age
#> 4 1 1 treatment treatment
#> domain_start domain_end mean_feature_frequency
#> 1 1100 2500 0.4166667
#> 2 1100 2500 0.0500000
#> 3 age age 0.0000000
#> 4 treatment treatment 0.2500000
#> max_feature_frequency selected_features
#> 1 0.750 2
#> 2 0.125 0
#> 3 0.000 0
#> 4 0.250 0
#> group_frequency group_selected
#> 1 0.750 TRUE
#> 2 0.125 FALSE
#> 3 0.000 FALSE
#> 4 0.250 FALSEInterval summaries can be requested directly:
if (requireNamespace("glmnet", quietly = TRUE)) {
fit_interval <- interval_stability_selection(
x = design,
selector = "lasso",
width = 4,
B = 8,
cutoff = 0.5,
seed = 1
)
head(selection_map(fit_interval, level = "group"))
}
#> predictor group_id group representation
#> 1 signal 1 signal[1:3] basis
#> 2 nuisance 2 nuisance[1:4] basis
#> 3 nuisance 3 nuisance[5:5] basis
#> 4 age 4 age[1:1] scalar
#> 5 treatment 5 treatment[1:1] scalar
#> basis_type source_representation n_features
#> 1 fpca grid 3
#> 2 spline grid 4
#> 3 spline grid 1
#> 4 scalar 1
#> 5 scalar 1
#> start_position end_position start_argval end_argval
#> 1 1 3 PC1 PC3
#> 2 1 4 B1 B4
#> 3 5 5 B5 B5
#> 4 1 1 age age
#> 5 1 1 treatment treatment
#> domain_start domain_end
#> 1 1100 2500
#> 2 1100 2464.10256410256
#> 3 1817.94871794872 2500
#> 4 age age
#> 5 treatment treatment
#> mean_feature_frequency max_feature_frequency
#> 1 0.4166667 0.750
#> 2 0.0625000 0.125
#> 3 0.0000000 0.000
#> 4 0.0000000 0.000
#> 5 0.2500000 0.250
#> selected_features group_frequency group_selected
#> 1 2 0.750 TRUE
#> 2 0 0.125 FALSE
#> 3 0 0.000 FALSE
#> 4 0 0.000 FALSE
#> 5 0 0.250 FALSE
#> interval_start interval_end interval_label
#> 1 1 3 signal[1:3]
#> 2 1 4 nuisance[1:4]
#> 3 5 5 nuisance[5:5]
#> 4 1 1 age[1:1]
#> 5 1 1 treatment[1:1]The validation layer can be used to compare FDA-aware
SelectBoost with a plain SelectBoost baseline
on the same simulated design and mapped truth.
sim <- simulate_fda_scenario(
n = 30,
grid_length = 20,
representation = "grid",
seed = 1
)
bench <- benchmark_selection_methods(
sim,
methods = c("selectboost", "plain_selectboost"),
levels = c("feature", "group"),
selectboost_args = list(B = 3, steps.seq = 0.5, c0lim = FALSE),
plain_selectboost_args = list(B = 3, steps.seq = 0.5, c0lim = FALSE)
)
head(bench$metrics)
#> level n_universe n_truth n_selected tp fp fn tn
#> 1 feature 42 9 34 9 25 0 8
#> 2 feature 42 9 38 9 29 0 4
#> 3 group 4 3 4 3 1 0 0
#> 4 group 4 3 4 3 1 0 0
#> precision recall specificity f1 jaccard
#> 1 0.2647059 1 0.2424242 0.4186047 0.2647059
#> 2 0.2368421 1 0.1212121 0.3829787 0.2368421
#> 3 0.7500000 1 0.0000000 0.8571429 0.7500000
#> 4 0.7500000 1 0.0000000 0.8571429 0.7500000
#> selection_rate c0 method
#> 1 0.8095238 c0 = 0.5 selectboost
#> 2 0.9047619 c0 = 0.5 plain_selectboost
#> 3 1.0000000 c0 = 0.5 selectboost
#> 4 1.0000000 c0 = 0.5 plain_selectboost
#> scenario representation family
#> 1 localized_dense grid gaussian
#> 2 localized_dense grid gaussian
#> 3 localized_dense grid gaussian
#> 4 localized_dense grid gaussianThe package also ships a larger saved sensitivity study under
inst/extdata/benchmarks/, generated by
tools/run_selectboost_sensitivity_study.R. The saved
top-setting table keeps the FDA benchmark settings together with the
mean F1 score of both algorithms.
benchmark_dir <- system.file("extdata", "benchmarks", package = "SelectBoost.FDA")
top_settings <- utils::read.csv(
file.path(benchmark_dir, "selectboost_sensitivity_top_settings.csv"),
stringsAsFactors = FALSE
)
utils::head(
top_settings[
,
c(
"scenario",
"confounding_strength",
"active_region_scale",
"local_correlation",
"association_method",
"bandwidth",
"selectboost_f1_mean",
"plain_selectboost_f1_mean",
"delta_mean",
"win_rate"
)
],
5
)
#> scenario confounding_strength
#> 1 confounded_blocks 0.6
#> 2 confounded_blocks 1.0
#> 3 confounded_blocks 0.6
#> 4 localized_dense 0.6
#> 5 confounded_blocks 0.6
#> active_region_scale local_correlation
#> 1 0.5 2
#> 2 0.8 2
#> 3 0.8 2
#> 4 0.5 2
#> 5 0.5 2
#> association_method bandwidth selectboost_f1_mean
#> 1 interval 8 0.5362319
#> 2 hybrid 4 0.5885135
#> 3 hybrid 4 0.5833671
#> 4 neighborhood 4 0.4972542
#> 5 hybrid 4 0.5429293
#> plain_selectboost_f1_mean delta_mean win_rate
#> 1 0.4087266 0.12750533 1.0000000
#> 2 0.4826750 0.10583853 1.0000000
#> 3 0.4944862 0.08888092 1.0000000
#> 4 0.4144859 0.08276831 0.6666667
#> 5 0.4657088 0.07722048 0.6666667In the shipped benchmark, the strongest gains appear in the
high-correlation, narrow-region settings. For example, in the
confounded_blocks scenario with
active_region_scale = 0.5,
local_correlation = 2, and interval grouping at
bandwidth = 8, the saved mean F1 values are
approximately 0.536 for FDA-aware SelectBoost
versus 0.409 for plain SelectBoost.
The package vignettes cover the main workflow families:
SelectBoost