SurvivalClusteringTree (version 1.1.1)

predict_distance_tree_matrix: Predict Distances Between Samples Based on a Survival Tree Fit (Data Supplied as Matrices)

Description

The function predict_distance_tree_matrix predicts distances between samples based on a survival tree fit.

Usage

predict_distance_tree_matrix(
  survival_tree,
  matrix_numeric,
  matrix_factor,
  missing = "omit"
)

Value

A list. node_distance gives the distance matrix between nodes. ind_distance gives the distance matrix between samples. ind_weights gives the weights of samples in each node.

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 random tree.

Details

Predict Distances Between Samples Based on a Survival Tree Fit (Data Supplied as Matrices) (Works for raw matrices)

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_distance<-
  predict_distance_tree_matrix(
    a_survival_tree,
    matrix_numeric=data.matrix(lung[,c(4,6:9),drop=FALSE]),
    matrix_factor=data.matrix(lung[,5,drop=FALSE]))