mb.coxmos: Multiblock COX Modeling Function

In Coxmos: Cox MultiBlock Survival

Multiblock COX Modeling Function

Description

This function provides a unified interface for all multiblock HD-COX modeling methods in the package.

Usage

mb.coxmos(
  method = c("sb.splsicox", "sb.splsdrcox", "sb.splsdrcox_penalty", "sb.splsdacox",
    "isb.splsicox", "isb.splsdrcox", "isb.splsdrcox_penalty", "isb.splsdacox",
    "mb.splsdrcox", "mb.splsdacox"),
  X,
  Y,
  cv.isb = NULL,
  design = NULL,
  x.center = TRUE,
  x.scale = FALSE,
  remove_near_zero_variance = TRUE,
  remove_zero_variance = TRUE,
  toKeep.zv = NULL,
  remove_non_significant = FALSE,
  alpha = 0.05,
  MIN_EPV = 5,
  returnData = TRUE,
  verbose = FALSE,
  n.comp = 4,
  penalty = 0.5,
  vector = NULL,
  MIN_NVAR = 1,
  MAX_NVAR = NULL,
  n.cut_points = 5,
  MIN_AUC_INCREASE = 0.01,
  EVAL_METHOD = "AUC",
  pred.method = "cenROC",
  max.iter = 200,
  times = NULL,
  max_time_points = 15
)

Arguments

method	Modeling method to use: "sb.splsicox", "sb.splsdrcox", "sb.splsdrcox_penalty", "sb.splsdacox", "isb.splsicox", "isb.splsdrcox", "isb.splsdrcox_penalty", "isb.splsdacox", "mb.splsdrcox", or "mb.splsdacox".
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.
cv.isb	Instance of class "Coxmos" and isb model. Used to retrieve the optimal components and variables for the sPLS Cox model (isb.splsicox, isb.splsdrcox, isb.splsdrcox_penalty and isb.splsdacox).
design	Numeric matrix. Matrix of size (number of blocks in X) x (number of blocks in X) with values between 0 and 1. Each value indicates the strength of the relationship to be modeled between two blocks; a value of 0 indicates no relationship, 1 is the maximum value. If NULL, auto-design is computed (default: NULL) (mb.splsdrcox and mb.splsdacox).
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_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).
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).
verbose	Logical. If verbose = TRUE, extra messages could be displayed (default: FALSE).
n.comp	Numeric. Number of latent components to compute for the (s)PLS model (default: 4).
penalty	Numeric. Penalty for variable selection for the individual cox models. Variables with a lower P-Value than 1 - "penalty" in the individual cox analysis will be keep for the approach (default: 0.5) (sb.splsicox and sb.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) (sb.splsdrcox, sb.splsdacox, mb.splsdrcox and mb.splsdacox).
MIN_NVAR	Numeric. Minimum range size for computing cut points to select the best number of variables to use (default: 1) (sb.splsdrcox, sb.splsdacox, mb.splsdrcox and mb.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) (sb.splsdrcox, sb.splsdacox, mb.splsdrcox and mb.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. For MB approaches as many as n.cut_points^n.blocks models will be computed as minimum (default: 5) (sb.splsdrcox, sb.splsdacox, mb.splsdrcox and mb.splsdacox).
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) (sb.splsdrcox, sb.splsdacox, mb.splsdrcox and mb.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: "AUC") (sb.splsdrcox, sb.splsdacox, mb.splsdrcox and mb.splsdacox).
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") (sb.splsdrcox, sb.splsdacox, mb.splsdrcox and mb.splsdacox).
max.iter	Numeric. Maximum number of iterations for PLS convergence (default: 200) (sb.splsdrcox, sb.splsdacox, mb.splsdrcox and mb.splsdacox).
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) (sb.splsdrcox, sb.splsdacox, mb.splsdrcox and mb.splsdacox).
max_time_points	Numeric. Maximum number of time points to use for evaluating the model (default: 15) (sb.splsdrcox, sb.splsdacox, mb.splsdrcox and mb.splsdacox).

Value

A Coxmos model of the specified multiblock type.

See Also

sb.splsicox for Single-Block SPLS-ICOX, sb.splsdrcox_penalty for Single-Block SPLS-DRCOX with penalty, sb.splsdrcox for Single-Block SPLS-DRCOX, sb.splsdacox for Single-Block SPLS-DACOX, isb.splsicox for Integrated Single-Block SPLS-ICOX, isb.splsdrcox_penalty for Integrated Single-Block SPLS-DRCOX with penalty, isb.splsdrcox for Integrated Single-Block SPLS-DRCOX, isb.splsdacox for Integrated Single-Block SPLS-DACOX, mb.splsdrcox for Multi-Block SPLS-DRCOX, mb.splsdacox for Multi-Block SPLS-DACOX

Examples

data("X_multiomic")
data("Y_multiomic")
X <- X_multiomic
set.seed(123)
index_train <- caret::createDataPartition(Y_multiomic$event, p = .25, list = FALSE, times = 1)
X_train <- X_multiomic
X_train$mirna <- X_train$mirna[index_train,1:30]
X_train$proteomic <- X_train$proteomic[index_train,1:30]
Y_train <- Y_multiomic[index_train,]
mb.coxmos(method = "sb.splsicox", X_train, Y_train, n.comp = 2, x.center = TRUE, x.scale = FALSE)

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