随机森林生存分析与 randomForestSRC
正如随机森林算法可以应用于回归和分类任务一样,它也可以扩展到生存分析。
在下面的示例中,生存模型适合并使用来自 CRAN 的包 randomForestSRC
进行预测,评分和性能分析。
require(randomForestSRC)
set.seed(130948) #Other seeds give similar comparative results
x1 <- runif(1000)
y <- rnorm(1000, mean = x1, sd = .3)
data <- data.frame(x1 = x1, y = y)
head(data)
x1 y 1 0.9604353 1.3549648 2 0.3771234 0.2961592 3 0.7844242 0.6942191 4 0.9860443 1.5348900 5 0.1942237 0.4629535 6 0.7442532 -0.0672639
(modRFSRC <- rfsrc(y ~ x1, data = data, ntree=500, nodesize = 5))
Sample size: 1000 Number of trees: 500 Minimum terminal node size: 5 Average no. of terminal nodes: 208.258 No. of variables tried at each split: 1 Total no. of variables: 1 Analysis: RF-R Family: regr Splitting rule: mse % variance explained: 32.08 Error rate: 0.11
x1new <- runif(10000)
ynew <- rnorm(10000, mean = x1new, sd = .3)
newdata <- data.frame(x1 = x1new, y = ynew)
survival.results <- predict(modRFSRC, newdata = newdata)
survival.results
Sample size of test (predict) data: 10000 Number of grow trees: 500 Average no. of grow terminal nodes: 208.258 Total no. of grow variables: 1 Analysis: RF-R Family: regr % variance explained: 34.97 Test set error rate: 0.11