Type: Package
Title: Clustering Analysis Using Survival Tree and Forest Algorithms
Version: 1.1.1
Date: 2024-05-15
Maintainer: Lu You <lu.you@epi.usf.edu>
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.
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
Suggests: knitr, rmarkdown, tinytest
Encoding: UTF-8
RoxygenNote: 7.3.1
Imports: Rcpp, survival, dplyr, grid, gridtext, formula.tools
LinkingTo: Rcpp, RcppArmadillo
VignetteBuilder: knitr
NeedsCompilation: yes
Packaged: 2024-05-24 02:58:58 UTC; luyou
Author: Lu You [aut, cre] (Created the package. Maintains the package.), Lauric Ferrat [aut] (Added functionality. Revised the package. Wrote the vignette.), Hemang Parikh [aut] (Checked and revised the package.), Yanan Huo [aut] (Revised plotting functions of the package.), Yuting Yang [aut] (Added some data frame features.), Jeffrey Krischer [ctb] (Supervisor the medical research. Coauthor of the medical manuscript.), Maria Redondo [ctb] (Principal investigators of the medical research. Coauthor of the medical manuscript.), Richard Oram [ctb] (Coauthor of the medical manuscript.), Andrea Steck [ctb] (Coauthor of the medical manuscript.)
Repository: CRAN
Date/Publication: 2024-05-24 21:10:25 UTC

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

Visualize the Fitted Survival Tree

Description

Visualize the Fitted Survival Tree

Usage

plot_survival_tree(survival_tree, cex = 0.75)

Arguments

survival_tree

a fitted survival tree object.

cex

numeric character expansion factor.

Value

No return value, called for generating graphical outputs.

Examples

library(survival)
a_survival_tree<-
  survival_tree(
  survival_outcome=Surv(time,status==2)~1,
  numeric_predictor=~age+ph.ecog+ph.karno+pat.karno+meal.cal,
  factor_predictor=~as.factor(sex),
  data=lung)
plot_survival_tree(a_survival_tree)

Predict Distances Between Samples Based on a Survival Forest Fit (Data Supplied as a Dataframe)

Description

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

Usage

predict_distance_forest(
  survival_forest,
  numeric_predictor,
  factor_predictor,
  data,
  missing = "omit"
)

Arguments

survival_forest

a fitted survival forest

numeric_predictor

a formula specifying the numeric predictors. As in ~x1+x2+x3, the three numeric variables x1, x2, and x3 are included as numeric predictors. x1[i], x2[i], and x3[i] are the predictors of the ith sample. The best practice is to use the same variables names in the training and testing dataset.

factor_predictor

a formula specifying the numeric predictors. As in ~z1+z2+z3, the three character variables z1, z2, and z3 are included as factor predictors. z1[i], z2[i], and z3[i] are the predictors of the ith sample. The best practice is to use the same variables names in the training and testing dataset.

data

the dataframe (test data) that stores the outcome and predictor variables. Variables in the global environment will be used if data is missing.

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 a Dataframe)

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.

Examples


library(survival)
a_survival_forest<-
  survival_forest(
    survival_outcome=Surv(time,status==2)~1,
    numeric_predictor=~age+ph.ecog+ph.karno+pat.karno+meal.cal,
    factor_predictor=~as.factor(sex),
    data=lung,nboot=20)
a_distance<-
  predict_distance_forest(
    a_survival_forest,
    numeric_predictor=~age+ph.ecog+ph.karno+pat.karno+meal.cal,
    factor_predictor=~as.factor(sex),
    data=lung)

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"
)

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)

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.

Examples


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]))

Predict Distances Between Samples Based on a Survival Tree Fit (Data Supplied as a Dataframe)

Description

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

Usage

predict_distance_tree(
  survival_tree,
  numeric_predictor,
  factor_predictor,
  data,
  missing = "omit"
)

Arguments

survival_tree

a fitted survival tree

numeric_predictor

a formula specifying the numeric predictors. As in ~x1+x2+x3, the three numeric variables x1, x2, and x3 are included as numeric predictors. x1[i], x2[i], and x3[i] are the predictors of the ith sample. The best practice is to use the same variables names in the training and testing dataset.

factor_predictor

a formula specifying the numeric predictors. As in ~z1+z2+z3, the three character variables z1, z2, and z3 are included as factor predictors. z1[i], z2[i], and z3[i] are the predictors of the ith sample. The best practice is to use the same variables names in the training and testing dataset.

data

the dataframe (test data) that stores the outcome and predictor variables. Variables in the global environment will be used if data is missing.

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 a Dataframe)

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.

Examples

library(survival)
a_survival_tree<-
  survival_tree(
    survival_outcome=Surv(time,status==2)~1,
    numeric_predictor=~age+ph.ecog+ph.karno+pat.karno+meal.cal,
    factor_predictor=~as.factor(sex),
    data=lung)
a_distance<-
  predict_distance_tree(
    a_survival_tree,
    numeric_predictor=~age+ph.ecog+ph.karno+pat.karno+meal.cal,
    factor_predictor=~as.factor(sex),
    data=lung)

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"
)

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)

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.

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]))

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

Description

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

Usage

predict_weights(
  survival_tree,
  numeric_predictor,
  factor_predictor,
  data,
  missing = "omit"
)

Arguments

survival_tree

a fitted survival tree

numeric_predictor

a formula specifying the numeric predictors. As in ~x1+x2+x3, the three numeric variables x1, x2, and x3 are included as numeric predictors. x1[i], x2[i], and x3[i] are the predictors of the ith sample. The best practice is to use the same variables names in the training and testing dataset.

factor_predictor

a formula specifying the numeric predictors. As in ~z1+z2+z3, the three character variables z1, z2, and z3 are included as factor predictors. z1[i], z2[i], and z3[i] are the predictors of the ith sample. The best practice is to use the same variables names in the training and testing dataset.

data

the dataframe (test data) that stores the outcome and predictor variables. Variables in the global environment will be used if data is missing.

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

Value

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

Examples

library(survival)
a_survival_tree<-
  survival_tree(
    survival_outcome=Surv(time,status==2)~1,
    numeric_predictor=~age+ph.ecog+ph.karno+pat.karno+meal.cal,
    factor_predictor=~as.factor(sex),
    data=lung)
a_weight<-
  predict_weights(
    a_survival_tree,
    numeric_predictor=~age+ph.ecog+ph.karno+pat.karno+meal.cal,
    factor_predictor=~as.factor(sex),
    data=lung)

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]))

Build a Survival Forest (Data Supplied as a Dataframe)

Description

The function survival_forest build a survival forest given the survival outcomes and predictors of numeric and factor variables.

Usage

survival_forest(
  survival_outcome,
  numeric_predictor,
  factor_predictor,
  weights = NULL,
  data,
  significance = 0.05,
  min_weights = 50,
  missing = "omit",
  test_type = "univariate",
  cut_type = 0,
  nboot = 100,
  seed = 0
)

Arguments

survival_outcome

a Surv object of right-censored outcomes. In Surv(time,event), time[i] is the survival time of the ith sample. event[i] is the survival event of the ith sample.

numeric_predictor

a formula specifying the numeric predictors. As in ~x1+x2+x3, the three numeric variables x1, x2, and x3 are included as numeric predictors. x1[i], x2[i], and x3[i] are the predictors of the ith sample.

factor_predictor

a formula specifying the numeric predictors. As in ~z1+z2+z3, the three character variables z1, z2, and z3 are included as factor predictors. z1[i], z2[i], and z3[i] are the predictors of the ith sample.

weights

sample weights, a numeric vector. weights[i] is the weight of the ith sample.

data

the dataframe that stores the outcome and predictor variables. Variables in the global environment will be used if data is missing.

significance

significance threshold, a numeric value. Stop the splitting algorithm when no splits give a p-value smaller than significance.

min_weights

minimum weight threshold, a numeric value. The weights in a node are greater than min_weights.

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.

test_type

a character value that specifies the type of statistical tests. If test_type=="univariate", then it performs a log-rank test without p-value adjustments. If test_type is in p.adjust.methods, i.e., one of holm, hochberg, hommel, bonferroni, BH, BY, or fdr, then the p-values will be adjusted using the corresponding method.

cut_type

an integer value that specifies how to cut between two numeric values. If cut_type==0, then cut at the ends. If cut_type==1, then cut from the middle. If cut_type==2, then cut randomly between the two values.

nboot

an integer value that specifies the number of bootstrap replications.

seed

an integer value that specifies the seed.

Details

Build a Survival Forest (Data Supplied as a Dataframe)

Value

A list containing the information of the survival forest fit.

Examples


library(survival)
a_survival_forest<-
  survival_forest(
    survival_outcome=Surv(time,status==2)~1,
    numeric_predictor=~age+ph.ecog+ph.karno+pat.karno+meal.cal,
    factor_predictor=~as.factor(sex),
    data=lung,nboot=20)

Build a Survival Forest (Data Supplied as Matrices)

Description

The function survival_forest_matrix build a survival forest given the survival outcomes and predictors of numeric and factor variables.

Usage

survival_forest_matrix(
  time,
  event,
  matrix_numeric,
  matrix_factor,
  weights = rep(1, length(time)),
  significance = 0.05,
  min_weights = 50,
  missing = "omit",
  test_type = "univariate",
  cut_type = 0,
  nboot = 100,
  seed = 0
)

Arguments

time

survival times, a numeric vector. time[i] is the survival time of the ith sample.

event

survival events, a logical vector. event[i] is the survival event of the ith sample.

matrix_numeric

numeric predictors, a numeric matrix. matrix_numeric[i,j] is the jth numeric predictor of the ith sample.

matrix_factor

factor predictors, a character matrix. matrix_factor[i,j] is the jth predictor of the ith sample.

weights

sample weights, a numeric vector. weights[i] is the weight of the ith sample.

significance

significance threshold, a numeric value. Stop the splitting algorithm when no splits give a p-value smaller than significance.

min_weights

minimum weight threshold, a numeric value. The weights in a node are greater than min_weights.

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.

test_type

a character value that specifies the type of statistical tests. If test_type=="univariate", then it performs a log-rank test without p-value adjustments. If test_type is in p.adjust.methods, i.e., one of holm, hochberg, hommel, bonferroni, BH, BY, or fdr, then the p-values will be adjusted using the corresponding method.

cut_type

an integer value that specifies how to cut between two numeric values. If cut_type==0, then cut at the ends. If cut_type==1, then cut from the middle. If cut_type==2, then cut randomly between the two values.

nboot

an integer value that specifies the number of bootstrap replications.

seed

an integer value that specifies the seed.

Details

Build a Survival Forest (Data Supplied as Matrices)

Value

A list containing the information of the survival forest fit.

Examples


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=FALSE]),
    nboot=20)

Build a Survival Tree (Data Supplied as a Dataframe)

Description

The function survival_tree build a survival tree given the survival outcomes and predictors of numeric and factor variables.

Usage

survival_tree(
  survival_outcome,
  numeric_predictor,
  factor_predictor,
  weights = NULL,
  data,
  significance = 0.05,
  min_weights = 50,
  missing = "omit",
  test_type = "univariate",
  cut_type = 0
)

Arguments

survival_outcome

a Surv object of right-censored outcomes. In Surv(time,event), time[i] is the survival time of the ith sample. event[i] is the survival event of the ith sample.

numeric_predictor

a formula specifying the numeric predictors. As in ~x1+x2+x3, the three numeric variables x1, x2, and x3 are included as numeric predictors. x1[i], x2[i], and x3[i] are the predictors of the ith sample.

factor_predictor

a formula specifying the numeric predictors. As in ~z1+z2+z3, the three character variables z1, z2, and z3 are included as factor predictors. z1[i], z2[i], and z3[i] are the predictors of the ith sample.

weights

sample weights, a numeric vector. weights[i] is the weight of the ith sample.

data

the dataframe that stores the outcome and predictor variables. Variables in the global environment will be used if data is missing.

significance

significance threshold, a numeric value. Stop the splitting algorithm when no splits give a p-value smaller than significance.

min_weights

minimum weight threshold, a numeric value. The weights in a node are greater than min_weights.

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.

test_type

a character value that specifies the type of statistical tests. If test_type=="univariate", then it performs a log-rank test without p-value adjustments. If test_type is in p.adjust.methods, i.e., one of holm, hochberg, hommel, bonferroni, BH, BY, or fdr, then the p-values will be adjusted using the corresponding method.

cut_type

an integer value that specifies how to cut between two numeric values. If cut_type==0, then cut at the ends. If cut_type==1, then cut from the middle. If cut_type==2, then cut randomly between the two values.

Details

Build a Survival Tree (Data Supplied as a Dataframe)

Value

A list containing the information of the survival tree fit.

Examples

library(survival)
a_survival_tree<-
  survival_tree(
    survival_outcome=Surv(time,status==2)~1,
    numeric_predictor=~age+ph.ecog+ph.karno+pat.karno+meal.cal,
    factor_predictor=~as.factor(sex),
    data=lung)

Build a Survival Tree (Data Supplied as Matrices)

Description

The function survival_tree_matrix build a survival tree given the survival outcomes and predictors of numeric and factor variables.

Usage

survival_tree_matrix(
  time,
  event,
  matrix_numeric,
  matrix_factor,
  weights = rep(1, length(time)),
  significance = 0.05,
  min_weights = 50,
  missing = "omit",
  test_type = "univariate",
  cut_type = 0
)

Arguments

time

survival times, a numeric vector. time[i] is the survival time of the ith sample.

event

survival events, a logical vector. event[i] is the survival event of the ith sample.

matrix_numeric

numeric predictors, a numeric matrix. matrix_numeric[i,j] is the jth numeric predictor of the ith sample.

matrix_factor

factor predictors, a character matrix. matrix_factor[i,j] is the jth predictor of the ith sample.

weights

sample weights, a numeric vector. weights[i] is the weight of the ith sample.

significance

significance threshold, a numeric value. Stop the splitting algorithm when no splits give a p-value smaller than significance.

min_weights

minimum weight threshold, a numeric value. The weights in a node are greater than min_weights.

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.

test_type

a character value that specifies the type of statistical tests. If test_type=="univariate", then it performs a log-rank test without p-value adjustments. If test_type is in p.adjust.methods, i.e., one of holm, hochberg, hommel, bonferroni, BH, BY, or fdr, then the p-values will be adjusted using the corresponding method.

cut_type

an integer value that specifies how to cut between two numeric values. If cut_type==0, then cut at the ends. If cut_type==1, then cut from the middle. If cut_type==2, then cut randomly between the two values.

Details

Build a Survival Tree (Data Supplied as Matrices)

Value

A list containing the information of the survival tree fit.