# 安装并加载randomForest包 # install.packages("randomForest") library(randomForest) # 读取数据集 data <- read.csv("IPIafter.csv") # 创建数据集data data <- data.frame( gender = data$gender, age = data$age, height = data$height, weight = data$weight, opreat.or.not = data$opreat.or.not, history.ill = data$history.ill, smoking = data$smoking, drinking = data$drinking, PONV = data$PONV, history.yundong = data$history.yundong, B.or.R = data$B.or.R, IPI00 = data$IPI00, IPI005 = data$IPI005, IPI1 = data$IPI1, IPIjinjing = data$IPIjinjing, IPI015 = data$IPI015, IPI2 = data$IPI2, IPI025 = data$IPI025, IPI3 = data$IPI3 ) # 构建随机森林模型 model <- randomForest(IPI005 + IPI1 + IPIjinjing + IPI015 + IPI2 + IPI025 + IPI3 ~ ., data = data) # 新数据 new_data <- data.frame( gender = c("F", "F", "M", "F", "F", "F", "M"), age = c(72, 61, 58, 65, 55, 47, 55), height = c(158, 159, 169, 154, 160, 162, 178), weight = c(50, 70, 83, 60, 60, 67, 105), opreat.or.not = c(0, 0, 0, 0, 0, 1, 0), history.ill = c(0, 0, 0, 1, 0, 1, 0), smoking = c("never", "never", "never", "never", "never", "never", "never"), drinking = c(0, 0, 0, 0, 0, 0, 0), PONV = c(0, 0, 0, 0, 0, 0, 0), history.yundong = c(0, 0, 0, 0, 0, 0, 0), B.or.R = c("B", "B", "R", "R", "R", "R", "R"), IPI00 = c(10, 10, 9, 6, 10, 10, 7), IPI005 = NA, # 新数据的目标变量待预测 IPI1 = NA, IPIjinjing = NA, IPI015 = NA, IPI2 = NA, IPI025 = NA, IPI3 = NA ) # 预测数据集中的观测值 new_predictions <- predict(model, newdata = new_data) # 打印预测结果 print(new_predictions) 根据我这个改写
时间: 2024-04-22 07:26:32 浏览: 24
# 安装并加载randomForest包
library(randomForest)
# 读取数据集
data <- read.csv("IPIafter.csv")
# 创建数据集data
data <- data.frame(
gender = data$gender,
age = data$age,
height = data$height,
weight = data$weight,
opreat.or.not = data$opreat.or.not,
history.ill = data$history.ill,
smoking = data$smoking,
drinking = data$drinking,
PONV = data$PONV,
history.yundong = data$history.yundong,
B.or.R = data$B.or.R,
IPI00 = data$IPI00,
IPI005 = data$IPI005,
IPI1 = data$IPI1,
IPIjinjing = data$IPIjinjing,
IPI015 = data$IPI015,
IPI2 = data$IPI2,
IPI025 = data$IPI025,
IPI3 = data$IPI3
)
# 构建随机森林模型
model <- randomForest(IPI005 + IPI1 + IPIjinjing + IPI015 + IPI2 + IPI025 + IPI3 ~ ., data = data)
# 新数据
new_data <- data.frame(
gender = c("F", "F", "M", "F", "F", "F", "M"),
age = c(72, 61, 58, 65, 55, 47, 55),
height = c(158, 159, 169, 154, 160, 162, 178),
weight = c(50, 70, 83, 60, 60, 67, 105),
opreat.or.not = c(0, 0, 0, 0, 0, 1, 0),
history.ill = c(0, 0, 0, 1, 0, 1, 0),
smoking = c("never", "never", "never", "never", "never", "never", "never"),
drinking = c(0, 0, 0, 0, 0, 0, 0),
PONV = c(0, 0, 0, 0, 0, 0, 0),
history.yundong = c(0, 0, 0, 0, 0, 0, 0),
B.or.R = c("B", "B", "R", "R", "R", "R", "R"),
IPI00 = c(10, 10, 9, 6, 10, 10, 7),
IPI005 = NA, # 新数据的目标变量待预测
IPI1 = NA,
IPIjinjing = NA,
IPI015 = NA,
IPI2 = NA,
IPI025 = NA,
IPI3 = NA
)
# 预测数据集中的观测值
new_predictions <- predict(model, newdata = new_data)
# 打印预测结果
print(new_predictions)
相关推荐
![zip](https://img-home.csdnimg.cn/images/20210720083736.png)
![docx](https://img-home.csdnimg.cn/images/20210720083331.png)
![docx](https://img-home.csdnimg.cn/images/20210720083331.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)