如何在gbm封装分析中计算生存函数？

# 导入所需的库
library(survival)
library(gbm)

# 导入数据集
data <- read.csv("data.csv")

# 创建生存对象
surv_obj <- with(data, Surv(time, event))

# 拆分数据集为训练集和测试集
set.seed(123)
train_index <- sample(1:nrow(data), 0.7*nrow(data))
train_data <- data[train_index, ]
test_data <- data[-train_index, ]

# 训练梯度提升机模型
gbm_model <- gbm(surv_obj ~ ., data = train_data, distribution = "coxph",
                 n.trees = 100, interaction.depth = 3, shrinkage = 0.01)

# 在测试集上进行预测
surv_pred <- predict(gbm_model, newdata = test_data, type = "response", n.trees = 100)

# 计算生存函数
surv_func <- survfit(surv_obj ~ surv_pred)

# 打印生存函数
print(surv_func)

# 计算C-索引
c_index <- survConcordance(surv_pred, surv_obj)
print(c_index)

# 进行log-rank检验
logrank_test <- survdiff(surv_obj ~ surv_pred)
print(logrank_test)