predict_weights_matrix {SurvivalClusteringTree}

Predict Weights of Samples in Terminal Nodes Based on a Survival Tree Fit (Data Supplied as Matrices)

Description

The function predict_weights_matrix predicts weights of samples in terminal nodes based on a survival tree fit.

Usage

predict_weights_matrix(
  survival_tree,
  matrix_numeric,
  matrix_factor,
  missing = "majority"
)

Arguments

survival_tree

a fitted survival tree

matrix_numeric

numeric predictors, a numeric matrix. matrix_numeric[i,j] is the jth numeric predictor of the ith sample. The best practice is to have the same column names in the training and testing dataset.

matrix_factor

factor predictors, a character matrix. matrix_factor[i,j] is the jth predictor of the ith sample. The best practice is to have the same column names in the training and testing dataset.

missing

a character value that specifies the handling of missing data. If missing=="omit", samples with missing values in the splitting variables will be discarded. If missing=="majority", samples with missing values in the splitting variables will be assigned to the majority node. If missing=="weighted", samples with missing values in the splitting variables will be weighted by the weights of branch nodes. The best practice is to use the same method as the trained tree.

Details

Predict Weights of Samples in Terminal Nodes Based on a Survival Tree Fit (Data Supplied as Matrices)

Value

A weight matrix representing the weights of samples in each node.

Examples

library(survival)
a_survival_tree<-
  survival_tree_matrix(
    time=lung$time,
    event=lung$status==2,
    matrix_numeric=data.matrix(lung[,c(4,6:9),drop=FALSE]),
    matrix_factor=data.matrix(lung[,5,drop=FALSE]))
a_weight<-
  predict_weights_matrix(
    a_survival_tree,
    matrix_numeric=data.matrix(lung[,c(4,6:9),drop=FALSE]),
    matrix_factor=data.matrix(lung[,5,drop=FALSE]))