R语言中，支持计算GBM模型，COXBOOST模型，SVM模型的BRIER SCORE 和变量重要性的包以及详细代码

在R语言中，有多个包可以支持计算GBM模型，COXBOOST模型，SVM模型的BRIER SCORE和变量重要性。以下是一些常用的包和相应的代码示例：

1. GBM模型

- 包：gbm

- 计算BRIER SCORE的代码：

  library(gbm)
  data(iris)
  set.seed(123)
  train <- sample(nrow(iris), 0.7 * nrow(iris))
  iris.gbm <- gbm(Species ~ ., data = iris[train,], distribution = "multinomial", n.trees = 1000, interaction.depth = 3, shrinkage = 0.01)
  pred <- predict(iris.gbm, newdata = iris[-train,], n.trees = 1000)
  brier_score <- mean((pred - as.numeric(iris[-train,"Species"]))^2)
  print(brier_score)

- 计算变量重要性的代码：

  library(gbm)
  data(iris)
  set.seed(123)
  train <- sample(nrow(iris), 0.7 * nrow(iris))
  iris.gbm <- gbm(Species ~ ., data = iris[train,], distribution = "multinomial", n.trees = 1000, interaction.depth = 3, shrinkage = 0.01)
  summary(iris.gbm)

2. COXBOOST模型

- 包：CoxBoost

- 计算BRIER SCORE的代码：

  library(CoxBoost)
  data(veteran)
  set.seed(123)
  train <- sample(nrow(veteran), 0.7 * nrow(veteran))
  veteran.coxboost <- coxboost(Surv(time, status) ~ ., data = veteran[train,], nboost = 1000, maxdepth = 3, gamma = 0.1)
  pred <- predict(veteran.coxboost, newdata = veteran[-train,], type = "risk")
  brier_score <- mean((pred - veteran[-train,"status"])^2)
  print(brier_score)

- 计算变量重要性的代码：

  library(CoxBoost)
  data(veteran)
  set.seed(123)
  train <- sample(nrow(veteran), 0.7 * nrow(veteran))
  veteran.coxboost <- coxboost(Surv(time, status) ~ ., data = veteran[train,], nboost = 1000, maxdepth = 3, gamma = 0.1)
  coef(veteran.coxboost)

3. SVM模型

- 包：e1071

- 计算BRIER SCORE的代码：

  library(e1071)
  data(iris)
  set.seed(123)
  train <- sample(nrow(iris), 0.7 * nrow(iris))
  iris.svm <- svm(Species ~ ., data = iris[train,], kernel = "radial", gamma = 0.1, cost = 10)
  pred <- predict(iris.svm, newdata = iris[-train,])
  pred <- ifelse(pred == "versicolor", 1, 0)
  brier_score <- mean((pred - as.numeric(iris[-train,"Species"] == "versicolor"))^2)
  print(brier_score)

- 计算变量重要性的代码：

  library(e1071)
  data(iris)
  set.seed(123)
  train <- sample(nrow(iris), 0.7 * nrow(iris))
  iris.svm <- svm(Species ~ ., data = iris[train,], kernel = "radial", gamma = 0.1, cost = 10)
  summary(iris.svm)