R “中的错误”；“随机森林”；从插入符号包

我正在运行OS X 10.10.2（约塞米蒂）的机器上使用R-studio（版本0.98.994）应用插入符号包中的“随机林”。以下是我的代码：

library(caret)
data(iris)
inTrain <- createDataPartition(y=iris$Species, p=0.7, list=FALSE)
training <- iris[inTrain,]
testing <- iris[-inTrain,]

# Use o Random Forest do CARET
modFit <- train(Species ~ ., data=training, method="rf", prox=TRUE)
modFit

库（插入符号）
数据（iris）
inTrain您缺少
randomForest
库。它是
caret
中建议的库之一，也是
rf
方法的来源。安装后，它应该可以像这样正常工作：

library(randomForest)
library(caret)

data(iris)
inTrain <- createDataPartition(y=iris$Species, p=0.7, list=FALSE)
training <- iris[inTrain,]
testing <- iris[-inTrain,]

# Use o Random Forest do CARET
modFit <- train(Species ~ ., data=training, method="rf", prox=TRUE)
modFit

嗨，LyzandeR，谢谢你提供的信息。我点击了绿色箭头。酷！：）谢谢！愉快的询问和回答！！
library(randomForest)
library(caret)

data(iris)
inTrain <- createDataPartition(y=iris$Species, p=0.7, list=FALSE)
training <- iris[inTrain,]
testing <- iris[-inTrain,]

# Use o Random Forest do CARET
modFit <- train(Species ~ ., data=training, method="rf", prox=TRUE)
modFit

> modFit
Random Forest 

105 samples
  4 predictor
  3 classes: 'setosa', 'versicolor', 'virginica' 

No pre-processing
Resampling: Bootstrapped (25 reps) 

Summary of sample sizes: 105, 105, 105, 105, 105, 105, ... 

Resampling results across tuning parameters:

  mtry  Accuracy  Kappa  Accuracy SD  Kappa SD
  2     0.949     0.923  0.0290       0.0436  
  3     0.953     0.929  0.0305       0.0460  
  4     0.948     0.921  0.0297       0.0447  

Accuracy was used to select the optimal model using  the largest value.
The final value used for the model was mtry = 3.