| 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]