Frédéric Bertrand
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.
Main features
- FDA-native design objects built directly from raw curves, basis representations, FPCA scores, and scalar covariates.
- Train/test-safe preprocessing with identity transforms, standardization, spline-basis expansion, and FPCA.
- Grouped stability selection for functional blocks and interval summaries.
- FDA-aware
SelectBoostwrappers plus a plainSelectBoostbaseline. - Simulation, benchmark, and evaluation helpers with mapped ground truth.
- Shipped sensitivity-study benchmark summaries for direct mean
F1comparisons betweenselectboost_fda()and plainSelectBoost. - Seeded simulation and stability-selection workflows that keep RNG changes local to the function call.
Installation
You can install the development version from GitHub with:
# install.packages("remotes")
remotes::install_github("bertran7/SelectBoost.FDA")Some workflows rely on optional backends:
-
glmnetfor lasso-based grouped stability selection. -
grpregfor group lasso. -
SGLfor sparse-group lasso. -
FDboostandstabsfor the nativeFDbooststability-selection route.
A first FDA-native workflow
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 source_representation n_components
#> nuisance.spline nuisance basis spline grid 5
#> signal.fpca signal basis fpca grid 3
#> first_component last_component components domain_start domain_end
#> nuisance.spline B1 B5 B1, B2, B3, B4, B5 1100 2500
#> signal.fpca PC1 PC3 PC1, PC2, PC3 1100 2500FDA-aware SelectBoost
SelectBoost.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 basis_type source_representation n_features
#> 1 signal 1 signal basis fpca grid 3
#> 2 nuisance 2 nuisance basis spline grid 5
#> 3 age 3 age scalar scalar 1
#> 4 treatment 4 treatment scalar scalar 1
#> start_position end_position start_argval end_argval domain_start domain_end c0
#> 1 1 3 PC1 PC3 1100 2500 c0 = 0.6
#> 2 1 5 B1 B5 1100 2500 c0 = 0.6
#> 3 1 1 age age age age c0 = 0.6
#> 4 1 1 treatment treatment treatment treatment c0 = 0.6
#> mean_selection max_selection selected_features
#> 1 0.6666667 1.00 2
#> 2 0.3000000 0.50 4
#> 3 0.7500000 0.75 1
#> 4 1.0000000 1.00 1Grouped stability selection
Grouped 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 basis_type source_representation n_features
#> 1 signal 1 signal basis fpca grid 3
#> 2 nuisance 2 nuisance basis spline grid 5
#> 3 age 3 age scalar scalar 1
#> 4 treatment 4 treatment scalar scalar 1
#> start_position end_position start_argval end_argval domain_start domain_end
#> 1 1 3 PC1 PC3 1100 2500
#> 2 1 5 B1 B5 1100 2500
#> 3 1 1 age age age age
#> 4 1 1 treatment treatment treatment treatment
#> mean_feature_frequency max_feature_frequency selected_features group_frequency
#> 1 0.4166667 0.750 2 0.750
#> 2 0.0500000 0.125 0 0.125
#> 3 0.0000000 0.000 0 0.000
#> 4 0.2500000 0.250 0 0.250
#> group_selected
#> 1 TRUE
#> 2 FALSE
#> 3 FALSE
#> 4 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 basis_type source_representation
#> 1 signal 1 signal[1:3] basis fpca grid
#> 2 nuisance 2 nuisance[1:4] basis spline grid
#> 3 nuisance 3 nuisance[5:5] basis spline grid
#> 4 age 4 age[1:1] scalar scalar
#> 5 treatment 5 treatment[1:1] scalar scalar
#> n_features start_position end_position start_argval end_argval domain_start
#> 1 3 1 3 PC1 PC3 1100
#> 2 4 1 4 B1 B4 1100
#> 3 1 5 5 B5 B5 1817.94871794872
#> 4 1 1 1 age age age
#> 5 1 1 1 treatment treatment treatment
#> domain_end mean_feature_frequency max_feature_frequency selected_features
#> 1 2500 0.4166667 0.750 2
#> 2 2464.10256410256 0.0625000 0.125 0
#> 3 2500 0.0000000 0.000 0
#> 4 age 0.0000000 0.000 0
#> 5 treatment 0.2500000 0.250 0
#> group_frequency group_selected interval_start interval_end interval_label
#> 1 0.750 TRUE 1 3 signal[1:3]
#> 2 0.125 FALSE 1 4 nuisance[1:4]
#> 3 0.000 FALSE 5 5 nuisance[5:5]
#> 4 0.000 FALSE 1 1 age[1:1]
#> 5 0.250 FALSE 1 1 treatment[1:1]Benchmarking on simulated FDA designs
The validation layer can be used to compare FDA-aware SelectBoost with a plain SelectBoost baseline on the same simulated design and mapped truth. When you pass seed=, the package uses a local seeded scope and does not leave the global RNG state changed after the call returns.
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 precision recall specificity f1
#> 1 feature 42 9 35 9 26 0 7 0.2571429 1 0.2121212 0.4090909
#> 2 feature 42 9 36 9 27 0 6 0.2500000 1 0.1818182 0.4000000
#> 3 group 4 3 4 3 1 0 0 0.7500000 1 0.0000000 0.8571429
#> 4 group 4 3 4 3 1 0 0 0.7500000 1 0.0000000 0.8571429
#> jaccard selection_rate c0 method scenario representation
#> 1 0.2571429 0.8333333 c0 = 0.5 selectboost localized_dense grid
#> 2 0.2500000 0.8571429 c0 = 0.5 plain_selectboost localized_dense grid
#> 3 0.7500000 1.0000000 c0 = 0.5 selectboost localized_dense grid
#> 4 0.7500000 1.0000000 c0 = 0.5 plain_selectboost localized_dense grid
#> family
#> 1 gaussian
#> 2 gaussian
#> 3 gaussian
#> 4 gaussianThe package also ships a larger saved sensitivity study under inst/extdata/benchmarks/, generated by tools/run_selectboost_sensitivity_study.R. That script writes to an explicit --output-dir=... path when supplied, and otherwise defaults to a subdirectory of tempdir(), so it does not write into the package directory by default. The committed files under inst/extdata/benchmarks/ are a saved copy of one benchmark run. The 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 active_region_scale local_correlation
#> 1 confounded_blocks 0.6 0.5 2
#> 2 confounded_blocks 1.0 0.8 2
#> 3 confounded_blocks 0.6 0.8 2
#> 4 localized_dense 0.6 0.5 2
#> 5 confounded_blocks 0.6 0.5 2
#> association_method bandwidth selectboost_f1_mean plain_selectboost_f1_mean delta_mean
#> 1 interval 8 0.5362319 0.4087266 0.12750533
#> 2 hybrid 4 0.5885135 0.4826750 0.10583853
#> 3 hybrid 4 0.5833671 0.4944862 0.08888092
#> 4 neighborhood 4 0.4972542 0.4144859 0.08276831
#> 5 hybrid 4 0.5429293 0.4657088 0.07722048
#> win_rate
#> 1 1.0000000
#> 2 1.0000000
#> 3 1.0000000
#> 4 0.6666667
#> 5 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.
Further documentation
The package vignettes cover the main workflow families:
- discretized curves
- spectra and interval-aware
SelectBoost - basis and FPCA workflows
- methods, calibration, and formula interfaces
- simulation and benchmark workflows
References
- Bertrand F., Aouadi I., Jung N., Carapito R., Vallat L., Bahram S., and Maumy-Bertrand M. SelectBoost: a general algorithm to enhance the performance of variable selection methods in correlated datasets. Bioinformatics. doi:10.1093/bioinformatics/btaa855
- Hofner B., Boccuto L., and Göker M. Stability selection and related subsampling-based selection procedures.
- Brockhaus S., Melcher M., Leisch F., and Greven S. FDboost: boosting functional regression models.