Sparse Group LASSO regularization for survival analysis
Usage
sparsegrouplasso(
data,
time_var,
event_var,
pred_vars,
group_definition = "factor_based",
custom_groups = "",
pathway_info,
correlation_threshold = 0.7,
alpha_sgl = 0.95,
lambda_sequence = "auto",
custom_lambda = "",
lambda_min_ratio = 0.001,
n_lambda = 100,
selection_criterion = "cv_deviance",
cv_folds = 10,
cv_repeats = 1,
ebic_gamma = 1,
weight_type = "none",
weight_power = 1,
standardize_vars = TRUE,
center_vars = TRUE,
orthogonalize_groups = FALSE,
max_iterations = 1000,
convergence_threshold = 1e-06,
warm_start = TRUE,
parallel_cv = FALSE,
seed_value = 42,
show_summary = TRUE,
show_coefficients = TRUE,
show_groups = TRUE,
show_path = FALSE,
show_performance = TRUE,
show_validation = TRUE,
plot_cv_error = TRUE,
plot_coefficients = TRUE,
plot_groups = TRUE,
plot_sparsity = FALSE,
plot_stability = FALSE,
alpha_level = 0.05,
confidence_intervals = FALSE,
bootstrap_samples = 500,
stability_selection = FALSE,
stability_threshold = 0.8,
stability_subsample = 0.8,
showExplanations = TRUE
)Arguments
- data
the data as a data frame
- time_var
the time-to-event variable
- event_var
the event indicator variable (0/1 or FALSE/TRUE)
- pred_vars
the predictor variables for the model
- group_definition
method for defining variable groups
- custom_groups
custom group specification as comma-separated lists. Example: "1,2;3,4,5;6" for three groups
- pathway_info
variable containing pathway/cluster information for grouping
- correlation_threshold
correlation threshold for correlation-based grouping
- alpha_sgl
mixing parameter between group LASSO (0) and LASSO (1). Default 0.95 emphasizes sparsity within groups
- lambda_sequence
method for selecting regularization parameter sequence
- custom_lambda
custom lambda values as comma-separated numbers. Example: "0.001,0.01,0.1,1"
- lambda_min_ratio
ratio of smallest to largest lambda value
- n_lambda
number of lambda values in the sequence
- selection_criterion
criterion for selecting optimal lambda
- cv_folds
number of folds for cross-validation
- cv_repeats
number of repeated cross-validation runs
- ebic_gamma
gamma parameter for Extended BIC (0 = standard BIC)
- weight_type
type of adaptive weights for penalty terms
- weight_power
power parameter for adaptive weights
- standardize_vars
whether to standardize predictor variables
- center_vars
whether to center predictor variables
- orthogonalize_groups
whether to orthogonalize variables within groups
- max_iterations
maximum number of iterations for optimization
- convergence_threshold
convergence threshold for optimization
- warm_start
whether to use warm start for lambda sequence
- parallel_cv
whether to use parallel processing for CV
- seed_value
random seed for reproducible results
- show_summary
show model summary table
- show_coefficients
show coefficient estimates table
- show_groups
show group structure table
- show_path
show complete regularization path
- show_performance
show model performance metrics
- show_validation
show cross-validation results
- plot_cv_error
plot cross-validation error curve
- plot_coefficients
plot coefficient regularization path
- plot_groups
plot group selection pattern
- plot_sparsity
plot sparsity pattern across lambda values
- plot_stability
plot stability selection results
- alpha_level
significance level for confidence intervals
- confidence_intervals
whether to calculate bootstrap confidence intervals
- bootstrap_samples
number of bootstrap samples for confidence intervals
- stability_selection
whether to perform stability selection
- stability_threshold
threshold for stability selection
- stability_subsample
subsample ratio for stability selection
- showExplanations
show explanations for the analysis
Value
A results object containing:
results$instructions | a html | ||||
results$todo | a html | ||||
results$summary | a table | ||||
results$coefficients | a table | ||||
results$groupStructure | a table | ||||
results$solutionPath | a table | ||||
results$performance | a table | ||||
results$validationResults | a table | ||||
results$adaptiveWeights | a table | ||||
results$stabilityResults | a table | ||||
results$comparisonTable | a table | ||||
results$cvErrorPlot | an image | ||||
results$coefficientPlot | an image | ||||
results$groupSelectionPlot | an image | ||||
results$sparsityPlot | an image | ||||
results$stabilityPlot | an image | ||||
results$explanations | a html |
Tables can be converted to data frames with asDF or as.data.frame. For example:
results$summary$asDF
as.data.frame(results$summary)