Error in X %*% init : 
  verlangt numerische/komplexe Matrix/Vektor-Argumente

Gradient Boosting with Componentwise Linear Models and automatically 
    tuned mstop.

`family` has been set to `CoxPH()` by default.

Argument of: mboost::boost_control

a character indicating how the empirical risk should be computed for each boosting iteration. inbag leads to risks computed for the learning sample (i.e., all non-zero weights), oobag to risks based on the out-of-bag (all observations with zero weights) and none to no risk computations at all.

Argument of: mboost::CoxPH

a vector containing the end-points of the interval to be searched for the minimum risk w.r.t. the nuisance parameter. In case of PropOdds, the starting values for the nuisance parameters.

Argument of: mboost::boost_control

an integer giving the number of initial boosting iterations. If mstop = 0, the offset model is returned.

Argument of: mboost::boost_control

a double (between 0 and 1) defining the step size or shrinkage parameter. The default is probably too large for many applications with family = Poisson() and a smaller value is better.

Argument of: mboost::boost_control

a logical that defines if the boosting algorithm stops internally when the out-of-bag risk in one iteration is larger than the out-of-bag risk in the iteration before. Can also be a positive number giving the risk difference that needs to be exceeded.

Argument of: mboost::glmboost

logical indicating of the predictor variables are centered before fitting.

Argument of: mboost::boost_control

a logical triggering printout of status information during the fitting process.

Argument of: mboost::glmboost

a Family object.

Fraction of all pairs of subjects whose predicted survival times are correctly ordered among all subjects that can actually be ordered. In other words, it is the probability of concordance between the predicted and the observed survival. Corrected by weighting with IPCW as suggested by Uno. Implemented in survAUC::UnoC.

Integrated brier score using Kaplan-Meier estimator for weighting