predict_weights: Predict Weights of Samples in Terminal Nodes Based on a...

In SurvivalClusteringTree: Clustering Analysis Using Survival Tree and Forest Algorithms

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)

SurvivalClusteringTree documentation built on May 29, 2024, 11:23 a.m.