SurvivalClusteringTree-package {SurvivalClusteringTree} | R Documentation |
Clustering Analysis Using Survival Tree and Forest Algorithms
Description
An outcome-guided algorithm is developed to identify clusters of samples with similar characteristics and survival rate. The algorithm first builds a random forest and then defines distances between samples based on the fitted random forest. Given the distances, we can apply hierarchical clustering algorithms to define clusters. Details about this method is described in <https://github.com/luyouepiusf/SurvivalClusteringTree>.
Package Content
Index of help topics:
SurvivalClusteringTree-package Clustering Analysis Using Survival Tree and Forest Algorithms plot_survival_tree Visualize the Fitted Survival Tree predict_distance_forest Predict Distances Between Samples Based on a Survival Forest Fit (Data Supplied as a Dataframe) predict_distance_forest_matrix Predict Distances Between Samples Based on a Survival Forest Fit (Data Supplied as Matrices) predict_distance_tree Predict Distances Between Samples Based on a Survival Tree Fit (Data Supplied as a Dataframe) predict_distance_tree_matrix Predict Distances Between Samples Based on a Survival Tree Fit (Data Supplied as Matrices) predict_weights Predict Weights of Samples in Terminal Nodes Based on a Survival Tree Fit (Data Supplied as a Dataframe) predict_weights_matrix Predict Weights of Samples in Terminal Nodes Based on a Survival Tree Fit (Data Supplied as Matrices) survival_forest Build a Survival Forest (Data Supplied as a Dataframe) survival_forest_matrix Build a Survival Forest (Data Supplied as Matrices) survival_tree Build a Survival Tree (Data Supplied as a Dataframe) survival_tree_matrix Build a Survival Tree (Data Supplied as Matrices)
Maintainer
Lu You <lu.you@epi.usf.edu>
Author(s)
NA
[Package SurvivalClusteringTree version 1.1.1 Index]