cv.coxmos: Cross-Validation for COX Models
In Coxmos: Cox MultiBlock Survival

cv.coxmos

R Documentation

Cross-Validation for COX Models

Description

This function provides a unified interface for cross-validation of all COX modeling methods in the package.

Usage

cv.coxmos(
  method = c("coxEN", "splsicox", "splsdrcox", "splsdrcox_penalty", "splsdacox"),
  X,
  Y,
  n_run = 3,
  k_folds = 10,
  x.center = TRUE,
  x.scale = FALSE,
  remove_near_zero_variance = TRUE,
  remove_zero_variance = TRUE,
  toKeep.zv = NULL,
  remove_variance_at_fold_level = FALSE,
  remove_non_significant_models = FALSE,
  remove_non_significant = FALSE,
  alpha = 0.05,
  w_AIC = 0,
  w_C.Index = 0,
  w_AUC = 1,
  w_I.BRIER = 0,
  times = NULL,
  max_time_points = 15,
  MIN_AUC_INCREASE = 0.01,
  MIN_AUC = 0.8,
  MIN_COMP_TO_CHECK = 3,
  pred.attr = "mean",
  pred.method = "cenROC",
  fast_mode = FALSE,
  MIN_EPV = 5,
  return_models = FALSE,
  returnData = FALSE,
  PARALLEL = FALSE,
  verbose = FALSE,
  seed = 123,
  EN.alpha.list = seq(0, 1, 0.2),
  max.ncomp = 8,
  penalty.list = seq(0.1, 0.9, 0.2),
  vector = NULL,
  MIN_NVAR = 1,
  MAX_NVAR = NULL,
  n.cut_points = 5,
  EVAL_METHOD = "AUC",
  max.iter = 200,
  max.variables = NULL
)

Arguments

`method`	Cross-validation method to use: "coxEN", "splsicox", "splsdrcox", "splsdrcox_penalty", or "splsdacox" (default: coxEN).
`X`	Numeric matrix or data.frame. Explanatory variables. Qualitative variables must be transform into binary variables.
`Y`	Numeric matrix or data.frame. Response variables. Object must have two columns named as "time" and "event". For event column, accepted values are: 0/1 or FALSE/TRUE for censored and event observations.
`n_run`	Numeric. Number of runs for cross validation (default: 3).
`k_folds`	Numeric. Number of folds for cross-validation (default: 10).
`x.center`	Logical. If x.center = TRUE, X matrix is centered to zero means (default: TRUE).
`x.scale`	Logical. If x.scale = TRUE, X matrix is scaled to unit variances (default: FALSE).
`remove_near_zero_variance`	Logical. If remove_near_zero_variance = TRUE, near zero variance variables will be removed (default: TRUE).
`remove_zero_variance`	Logical. If remove_zero_variance = TRUE, zero variance variables will be removed (default: TRUE).
`toKeep.zv`	Character vector. Name of variables in X to not be deleted by (near) zero variance filtering (default: NULL).
`remove_variance_at_fold_level`	Logical. If remove_variance_at_fold_level = TRUE, (near) zero variance will be removed at fold level. Not recommended. (default: FALSE).
`remove_non_significant_models`	Logical. If remove_non_significant_models = TRUE, non-significant models are removed before computing the evaluation. A non-significant model is a model with at least one component/variable with a P-Value higher than the alpha cutoff.
`remove_non_significant`	Logical. If remove_non_significant = TRUE, non-significant variables/components in final cox model will be removed until all variables are significant by forward selection (default: FALSE).
`alpha`	Numeric. Numerical values are regarded as significant if they fall below the threshold (default: 0.05).
`w_AIC`	Numeric. Weight for AIC evaluator. All weights must sum 1 (default: 0).
`w_C.Index`	Numeric. Weight for C-Index evaluator. All weights must sum 1 (default: 0).
`w_AUC`	Numeric. Weight for AUC evaluator. All weights must sum 1 (default: 1).
`w_I.BRIER`	Numeric. Weight for BRIER SCORE evaluator. All weights must sum 1 (default: 0).
`times`	Numeric vector. Time points where the AUC will be evaluated. If NULL, a maximum of 'max_time_points' points will be selected equally distributed (default: NULL).
`max_time_points`	Numeric. Maximum number of time points to use for evaluating the model (default: 15).
`MIN_AUC_INCREASE`	Numeric. Minimum improvement between different cross validation models to continue evaluating higher values in the multiple tested parameters. If it is not reached for next 'MIN_COMP_TO_CHECK' models and the minimum 'MIN_AUC' value is reached, the evaluation stops (default: 0.01).
`MIN_AUC`	Numeric. Minimum AUC desire to reach cross-validation models. If the minimum is reached, the evaluation could stop if the improvement does not reach an AUC higher than adding the 'MIN_AUC_INCREASE' value (default: 0.8).
`MIN_COMP_TO_CHECK`	Numeric. Number of penalties/components to evaluate to check if the AUC improves. If for the next 'MIN_COMP_TO_CHECK' the AUC is not better and the 'MIN_AUC' is meet, the evaluation could stop (default: 3).
`pred.attr`	Character. Way to evaluate the metric selected. Must be one of the following: "mean" or "median" (default: "mean").
`pred.method`	Character. AUC evaluation algorithm method for evaluate the model performance. Must be one of the following: "risksetROC", "survivalROC", "cenROC", "nsROC", "smoothROCtime_C", "smoothROCtime_I" (default: "cenROC").
`fast_mode`	Logical. If fast_mode = TRUE, for each run, only one fold is evaluated simultaneously. If fast_mode = FALSE, for each run, all linear predictors are computed for test observations. Once all have their linear predictors, the evaluation is perform across all the observations together (default: FALSE).
`MIN_EPV`	Numeric. Minimum number of Events Per Variable (EPV) you want reach for the final cox model. Used to restrict the number of variables/components can be computed in final cox models. If the minimum is not meet, the model cannot be computed (default: 5).
`return_models`	Logical. Return all models computed in cross validation (default: FALSE).
`returnData`	Logical. Return original and normalized X and Y matrices (default: TRUE).
`PARALLEL`	Logical. Run the cross validation with multicore option. As many cores as your total cores - 1 will be used. It could lead to higher RAM consumption (default: FALSE).
`verbose`	Logical. If verbose = TRUE, extra messages could be displayed (default: FALSE).
`seed`	Number. Seed value for performing runs/folds divisions (default: 123).
`EN.alpha.list`	Numeric vector. Elastic net mixing parameter values for cross-validation. If NULL, default seq(0,1,0.1) will be used (default: NULL) (coxEN).
`max.ncomp`	Numeric. Maximum number of latent components to compute for the (s)PLS model (default: NULL) (splsicox, splsdrcox, splsdrcox_penalty, splsdacox).
`penalty.list`	Numeric vector. Penalty values for variable selection. If NULL, default seq(0,0.9,0.1) will be used (default: NULL) (splsicox, splsdrcox_penalty).
`vector`	Numeric vector. Used for computing best number of variables. As many values as components have to be provided. If vector = NULL, an automatic detection is perform (default: NULL) (splsdrcox, splsdacox).
`MIN_NVAR`	Numeric. Minimum range size for computing cut points to select the best number of variables to use (default: NULL) (splsdrcox, splsdacox).
`MAX_NVAR`	Numeric. Maximum range size for computing cut points to select the best number of variables to use. If NULL, the number of variables is selected (default: NULL) (splsdrcox, splsdacox).
`n.cut_points`	Numeric. Number of cut points for searching the optimal number of variables. If only two cut points are selected, minimum and maximum size are used (default: NULL) (splsdrcox, splsdacox).
`EVAL_METHOD`	Character. The selected metric will be use to compute the best number of variables. Must be one of the following: "AUC", "IBS" or "C.Index" (default: NULL) (splsdrcox, splsdacox).
`max.iter`	Numeric. Maximum number of iterations for PLS convergence (default: NULL) (splsdrcox, splsdacox).
`max.variables`	Numeric. Maximum number of variables to keep in the cox model (default: NULL) (coxEN).

Value

A cross-validation object of the specified type.

Examples

data("X_proteomic")
data("Y_proteomic")
set.seed(123)
index_train <- caret::createDataPartition(Y_proteomic$event, p = .5, list = FALSE, times = 1)
X_train <- X_proteomic[index_train,1:50]
Y_train <- Y_proteomic[index_train,]

# Elastic Net Cross-Validation
# cv_model <- cv_coxmos(method = "coxEN", X = X_train, Y = Y_train, EN.alpha.list = seq(0, 1, 0.2))

Coxmos documentation built on June 8, 2025, 10:30 a.m.