Partial Least Squares Cox Models — plscox • ClinicoPath

Partial Least Squares Cox regression for high-dimensional survival data. Combines PLS dimensionality reduction with Cox proportional hazards modeling for analysis of genomic, proteomic, and other high-dimensional datasets.

Usage

plscox(
  data,
  time,
  status,
  outcomeLevel,
  censorLevel,
  predictors,
  pls_components = 5,
  cross_validation = "k10",
  component_selection = "cv_loglik",
  scaling_method = "standardize",
  tolerance = 1e-06,
  tie_method = "efron",
  sparse_pls = FALSE,
  limQ2set = 0.0975,
  pvals_expli = FALSE,
  alpha_pvals_expli = 0.05,
  bootstrap_validation = FALSE,
  n_bootstrap = 200,
  permutation_test = FALSE,
  n_permutations = 100,
  plot_components = TRUE,
  plot_loadings = TRUE,
  plot_scores = TRUE,
  plot_validation = TRUE,
  plot_survival = TRUE,
  risk_groups = 3,
  confidence_intervals = TRUE,
  feature_importance = TRUE,
  prediction_accuracy = TRUE,
  suitabilityCheck = TRUE
)

Arguments

data: The data as a data frame.
time: .
status: .
outcomeLevel: Level of status considered as the event. For binary factor outcomes, if left empty the second observed level is used; for numeric binary outcomes, the larger observed value is used (or 1 for 0/1 coding).
censorLevel: Level of status considered as censored (no event). Together with outcomeLevel, this defines a strict two-level encoding: rows whose status matches neither level are treated as missing and excluded.
predictors: .
pls_components: Number of PLS components to extract
cross_validation: Cross-validation method for component selection
component_selection: Method for selecting optimal number of components
scaling_method: Method for scaling predictor variables
tolerance: Tolerance for algorithm convergence (jamovi default: 1e-06, plsRcox default: 1e-12)
tie_method: Method for handling tied event times in Cox model (plsRcox default: efron)
sparse_pls: Enable sparse PLS for automatic variable selection (plsRcox default: false)
limQ2set: Q-squared threshold for PLS component stopping criterion (plsRcox default: 0.0975)
pvals_expli: Use p-value based predictor selection during PLS fitting (plsRcox default: false)
alpha_pvals_expli: Significance level for p-value based variable selection (plsRcox default: 0.05)
bootstrap_validation: Perform bootstrap validation of model performance
n_bootstrap: Number of bootstrap replications
permutation_test: Perform permutation test for variable importance
n_permutations: Number of permutations for significance testing
plot_components: Create PLS component visualization plots
plot_loadings: Display variable loadings for PLS components
plot_scores: Show component scores and survival relationships
plot_validation: Display cross-validation curves for component selection
plot_survival: Generate risk-stratified survival curves
risk_groups: Number of risk groups for survival stratification
confidence_intervals: Calculate confidence intervals for hazard ratios
feature_importance: Calculate and display variable importance scores
prediction_accuracy: Assess model prediction accuracy using C-index and other metrics
suitabilityCheck: Run a comprehensive data suitability assessment before analysis. Checks sample size, events-per-variable ratio, multicollinearity, and whether regularization is needed.

Value

A results object containing:

`results$todo`					a html
`results$suitabilityReport`					a html
`results$modelSummary`					a html
`results$componentSelection`					a table
`results$modelCoefficients`					a table
`results$variableLoadings`					a table
`results$modelPerformance`					a table
`results$riskStratification`					a table
`results$componentPlot`					an image
`results$loadingsPlot`					an image
`results$scoresPlot`					an image
`results$validationPlot`					an image
`results$survivalPlot`					an image
`results$bootstrapResults`					a html
`results$permutationResults`					a html
`results$clinicalGuidance`					a html
`results$technicalNotes`					a html

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

results$componentSelection$asDF

as.data.frame(results$componentSelection)