lassocox: Lasso-Cox Regression

In sbalci/ClinicoPathJamoviModule: Analysis for Clinicopathological Research

Lasso-Cox Regression

Description

Performs Lasso-penalized Cox regression for variable selection in survival analysis.

Usage

lassocox(
  data,
  elapsedtime,
  outcome,
  outcomeLevel,
  explanatory,
  lambda = "lambda.1se",
  nfolds = 10,
  standardize = TRUE,
  cv_plot = TRUE,
  coef_plot = TRUE,
  survival_plot = TRUE
)

Arguments

data	The data as a data frame.
elapsedtime	The numeric variable representing follow-up time until the event or last observation.
outcome	The outcome variable. Typically indicates event status (e.g., death, recurrence).
outcomeLevel	The level of outcome considered as the event.
explanatory	Variables to be considered for selection in the Lasso-Cox regression.
lambda	Method for selecting the optimal lambda parameter from cross-validation.
nfolds	Number of folds for cross-validation.
standardize	Whether to standardize predictor variables before fitting.
cv_plot	Whether to show the cross-validation plot.
coef_plot	Whether to show the coefficient path plot.
survival_plot	Whether to show survival curves by risk groups.

Value

A results object containing:

results$todo					a html
results$modelSummary					a table
results$coefficients					a table
results$performance					a table
results$cv_plot					an image
results$coef_plot					an image
results$survival_plot					an image
results$riskScore					an output

Tables can be converted to data frames with asDF or as.data.frame. For example:

results$modelSummary$asDF

as.data.frame(results$modelSummary)

Examples

# example will be added

sbalci/ClinicoPathJamoviModule documentation built on June 13, 2025, 9:34 a.m.