cv.isb.splsdrcox {Coxmos}

Cross validation cv.isb.splsdrcox

Description

This function performs cross-validated sparse partial least squares iterative single block for splsdrcox. The function returns the optimal number of components and the optimal sparsity penalty value based on cross-validation. The performance could be based on multiple metrics as Area Under the Curve (AUC), Brier Score or C-Index. Furthermore, the user could establish more than one metric simultaneously.

Usage

cv.isb.splsdrcox(
  X,
  Y,
  max.ncomp = 8,
  penalty.list = seq(0.1, 0.9, 0.2),
  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_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,
  returnData = TRUE,
  return_models = FALSE,
  PARALLEL = FALSE,
  verbose = FALSE,
  seed = 123
)

Arguments

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.

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-DRCOX. 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 (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_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).

returnData

Logical. Return original and normalized X and Y matrices (default: TRUE).

return_models

Logical. Return all models computed in cross validation (default: FALSE).

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).

Details

The cv.isb.splsdrcox function performs cross-validation for the integrative single-block sparse partial least squares deviance residual Cox analysis. Unlike the single-block (SB) approach, the integrative single-block (ISB) method allows for the consideration of multiple blocks of data, potentially from different sources or types, to be integrated into a single model. A key distinction of the ISB approach is its ability to compute and optimize hyperparameters individually for each block, rather than applying a uniform set of hyperparameters across all blocks. This ensures that each block's unique characteristics are taken into account, leading to a more tailored and potentially more accurate model.

Cross-validation is essential for assessing the generalizability of the model and avoiding overfitting. By partitioning the original dataset into training and test sets multiple times, the function evaluates the model's performance across different subsets of the data. This iterative process ensures that the model's performance is robust and not overly reliant on a specific partition of the data.

The function evaluates a range of hyperparameters, including the number of latent components (max.ncomp) and the penalty for variable selection (penalty.list). For each combination of hyperparameters, the dataset is divided into training and test sets based on the specified number of folds (k_folds). The model is then trained on the training set and its performance is assessed on the test set. This process is repeated for the specified number of runs (n_run), providing a comprehensive evaluation of the model's performance.

Various evaluation metrics, such as AIC, C-Index, Brier Score, and AUC, are computed for each combination of hyperparameters. These metrics provide insights into the model's accuracy, discriminative ability, and calibration. The function then identifies the optimal hyperparameters that yield the best performance based on these metrics.

In summary, the cv.isb.splsdrcox function offers a robust and integrative approach for hyperparameter tuning and model evaluation for the sparse partial least squares deviance residual Cox analysis. By allowing individualized hyperparameter optimization for each block, the ISB approach ensures a more nuanced and potentially more accurate model compared to the traditional SB method.

Value

Instance of class "Coxmos" and model "sb.splscox". The class contains the following elements: X: List of normalized X data information.

• (data): normalized X matrix

• (weightings): PLS weights

• (weightings_norm): PLS normalize weights

• (W.star): PLS W* vector

• (scores): PLS scores/variates

• (x.mean): mean values for X matrix

• (x.sd): standard deviation for X matrix

Y: List of normalized Y data information.

• (deviance_residuals): deviance residual vector used as Y matrix in the sPLS.

• (dr.mean): mean values for deviance residuals Y matrix

• (dr.sd): standard deviation for deviance residuals Y matrix'

• (data): normalized X matrix

• (y.mean): mean values for Y matrix

• (y.sd): standard deviation for Y matrix'

survival_model: List of survival model information.

• fit: coxph object.

• AIC: AIC of cox model.

• BIC: BIC of cox model.

• lp: linear predictors for train data.

• coef: Coefficients for cox model.

• YChapeau: Y Chapeau residuals.

• Yresidus: Y residuals.

list_spls_models: List of sPLS-DRCOX models computed for each block.

n.comp: Number of components selected.

penalty Penalty applied.

call: call function

X_input: X input matrix

Y_input: Y input matrix

nzv: Variables removed by remove_near_zero_variance or remove_zero_variance.

nz_coeffvar: Variables removed by coefficient variation near zero.

class: Model class.

time: time consumed for running the cox analysis.

Author(s)

Pedro Salguero Garcia. Maintainer: [e-mail]

Examples

data("X_multiomic")
data("Y_multiomic")
set.seed(123)
index_train <- caret::createDataPartition(Y_multiomic$event, p = .5, list = FALSE, times = 1)
X_train <- X_multiomic
X_train$mirna <- X_train$mirna[index_train,1:50]
X_train$proteomic <- X_train$proteomic[index_train,1:50]
Y_train <- Y_multiomic[index_train,]
isb.splsdrcox_model <- cv.isb.splsdrcox(X_train, Y_train, max.ncomp = 2, penalty.list = c(0.5),
n_run = 1, k_folds = 2, x.center = TRUE, x.scale = TRUE)