survival_forest {SurvivalClusteringTree}

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)