Performs penalized Cox proportional hazards regression for high-dimensional survival data using L1 (LASSO), L2 (Ridge), and Elastic Net regularization. This method is particularly useful when the number of covariates approaches or exceeds the number of observations, or when variable selection is needed.
Usage
penalizedcoxregression(
data,
time,
status,
predictors,
penaltyType = "lasso",
alphaValue = 0.5,
crossValidation = TRUE,
cvFolds = 10,
lambdaSelection = "lambda.1se",
customLambda = 0.01,
standardize = TRUE,
plotCoefficientPath = TRUE,
plotCrossValidation = TRUE,
variableSelection = TRUE
)Arguments
- data
the data as a data frame
- time
the time variable for survival analysis
- status
the status variable (0 = censored, 1 = event)
- predictors
predictor variables for the penalized Cox model
- penaltyType
the type of penalty to apply in regularization
- alphaValue
mixing parameter for elastic net (0 = ridge, 1 = lasso)
- crossValidation
whether to use cross-validation for penalty parameter selection
- cvFolds
number of folds for cross-validation
- lambdaSelection
criterion for selecting the regularization parameter
- customLambda
custom lambda value when using custom selection
- standardize
whether to standardize predictor variables
- plotCoefficientPath
whether to plot coefficient paths across lambda values
- plotCrossValidation
whether to plot cross-validation error curves
- variableSelection
whether to output selected variables and their importance
Value
A results object containing:
results$instructions | a html | ||||
results$modelSummary | a table | ||||
results$selectedVariables | a table | ||||
results$crossValidationResults | a table | ||||
results$penaltyPath | a table | ||||
results$coefficientPath | an image | ||||
results$crossValidationPlot | an image | ||||
results$analysisReport | a html |
Tables can be converted to data frames with asDF or as.data.frame. For example:
results$modelSummary$asDF
as.data.frame(results$modelSummary)
Details
Key features:
LASSO (L1) regularization for automatic variable selection
Ridge (L2) regularization for correlated predictors
Elastic Net combining L1 and L2 penalties
Cross-validation for optimal penalty parameter selection
Coefficient path visualization across penalty values
Variable importance ranking and selection
High-dimensional survival analysis capabilities
Examples
# Penalized Cox regression with LASSO
penalizedcoxregression(
data = data,
time = "time",
status = "status",
predictors = c("age", "gene1", "gene2", "treatment"),
penaltyType = "lasso",
crossValidation = TRUE
)