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使用插入符号进行生存分析(随机生存林)

[英]using caret for survival analysis (random survival forest)

Is there a way to use caret for Survival Analysis. 有没有办法使用caret进行生存分析。 I really like how easy to use it is. 我真的很喜欢它的易用性。 I tried fitting a random survival forest using the party package, which is on caret's list. 我尝试使用party包来安装随机生存林,这是在插入符号列表中。

This works: 这有效:

library(survival)
library(caret)
library(party)


fitcforest <- cforest(Surv(futime, death) ~ sex+age, data=flchain,
                     controls = cforest_classical(ntree = 1000))

but using caret I get an error: 但是使用caret我收到错误:

fitControl <- trainControl(## 10-fold CV
  method = "repeatedcv",
  number = 10,
  repeats = 2,
)

cforestfit <- train(Surv(futime, death) ~ sex+age,data=flchain, method="cforest",trControl = fitControl)

I get this error: 我收到此错误:

Error: nrow(x) == length(y) is not TRUE

Is there a way to make these Surv object work with caret? 有没有办法让这些Surv对象与插入符号一起使用? Can I use other survival analysis oriented packages with caret? 我可以使用其他以生存分析为导向的包装吗?

thanks 谢谢

Not yet. 还没。 That is one of two major updates that should be coming soon (the other expands pre-processing). 这是即将推出的两个主要更新之一(另一个扩展预处理)。

Contact me offline if you are interested in helping the development and/or testing of those features. 如果您有兴趣帮助开发和/或测试这些功能,请离线与我联系。

Thanks, 谢谢,

Max 马克斯

I have found no way to train survival models with caret. 我发现无法用插入符号训练生存模型。 As an alternative, the mlr framework (1) has a set of survival learners (2). 作为替代方案,mlr框架(1)具有一组生存学习者(2)。 I have found mlr to be extremely user-friendly and useful. 我发现mlr非常人性化且有用。

  1. mlr: http://mlr-org.github.io/mlr-tutorial/release/html/ mlr: http ://mlr-org.github.io/mlr-tutorial/release/html/
  2. survival learners in mlr: http://mlr-org.github.io/mlr-tutorial/release/html/integrated_learners/index.html#survival-analysis-15 mlr中的生存学习者: http ://mlr-org.github.io/mlr-tutorial/release/html/integrated_learners/index.html#survival-analysis-15

There is an increasing number of packages in R that model survival data, examples; R中存在越来越多的模型生存数据,例如;

For lasso and elastic nets: BioSpear. 套索和弹性网:BioSpear。

For random forest: randomForestSRC. 对于随机森林:randomForestSRC。

Best, Loic 最好的,Loic

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