Iterative SB.sPLS-ICOX-Dynamic Cross-Validation
This function performs cross-validated sparse partial least squares single-block for sPLS-ICOX-Dynamic. It returns the optimal number of components and the optimal sparsity penalty value based on cross-validation. Performance can be evaluated using multiple metrics, such as Area Under the Curve (AUC), I. Brier Score, or C-Index. Users can also specify more than one metric simultaneously.
cv.isb.splsicox( X, Y, max.ncomp = 8, penalty.list = seq(0, 0.9, 0.1), 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 )
X: List of numeric matrices or data.frames. 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.max.ncomp: Numeric. Maximum number of PLS components to compute for the cross validation (default: 8).penalty.list: Numeric vector. Vector of penalty values. Penalty for sPLS-DACOX. If penalty = 0 no penalty is applied, when penalty = 1 maximum penalty (no variables are selected) based on 'plsRcox' penalty. Equal or greater than 1 cannot be selected (default: seq(0.1,0.9,0.2)).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).An instance of class "Coxmos" and model "cv.SB.sPLS-ICOX-Dynamic", containing:
best_model_info: Data frame with the best model's information.df_results_folds: Data frame with fold-level results.df_results_runs: Data frame with run-level results.df_results_comps: Data frame with component-level results.list_cv_spls_models: List of cross-validated models for each block.opt.comp: Optimal number of components.opt.nvar: Optimal number of variables selected.class: Model class.time: Time taken to run the cross-validation.The cv.isb.splsicox function performs cross-validation for the single-block sparse partial least squares deviance residual Cox analysis (sPLS-ICOX). Cross-validation evaluates different hyperparameter combinations, including the number of components (max.ncomp) and the number of variables selected (vector). The function systematically evaluates models across multiple runs and folds to determine the best configuration. It allows flexibility in metrics, preprocessing steps (centering, scaling, variance filtering), and stopping criteria.
For each run, the dataset is divided into training and test sets for the specified number of folds (k_folds). Various metrics, such as AIC, C-Index, I. Brier Score, and AUC, are computed to assess model performance. The function identifies the optimal hyperparameters that yield the best performance based on the selected evaluation metrics.
Additionally, it offers options to control the evaluation algorithm method (pred.method), whether to return all models, and parallel processing (PARALLEL). The function also allows the user to control the verbosity of output messages and set the minimum threshold for Events Per Variable (MIN_EPV).
data("X_multiomic") data("Y_multiomic") set.seed(123) index_train <- caret::createDataPartition(Y_multiomic$event, p = .3, list = FALSE, times = 1) X_train <- X_multiomic X_train$mirna <- X_train$mirna[index_train,1:30] X_train$proteomic <- X_train$proteomic[index_train,1:30] Y_train <- Y_multiomic[index_train,] cv.isb.splsicox_model <- cv.isb.splsicox(X_train, Y_train, max.ncomp = 1, penalty.list = c(0, 0.5), n_run = 1, k_folds = 2, x.center = TRUE, x.scale = TRUE)
Pedro Salguero Garcia. Maintainer: pedsalga@upv.edu.es