SurvivalClusteringTree (version 1.1.1)

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

Description

The function predict_distance_forest_matrix predicts distances between samples based on a survival forest fit.

Usage

predict_distance_forest_matrix(
  survival_forest,
  matrix_numeric,
  matrix_factor,
  missing = "omit"
)

Value

A list. mean_distance is the mean distance matrix. sum_distance is the matrix that sums the distances between samples. sum_non_na is the matrix of the number of non NA distances being averaged.

Arguments

survival_forest

a fitted survival forest

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 forest.

Details

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

Examples

# \donttest{
library(survival)
a_survival_forest<-
  survival_forest_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=F]),
    nboot=20)
a_distance<-
  predict_distance_forest_matrix(
    a_survival_forest,
    matrix_numeric=data.matrix(lung[,c(4,6:9),drop=FALSE]),
    matrix_factor=data.matrix(lung[,5,drop=F]))
# }