Question

第20行出现问题：x3 <- lm(Salary ~ ...

as.data.frame.default（data）中的错误：无法将类“ c（“ train”，“ train.formula”）'强制转换为data.frame

如何解决？

attach(Hitters)
Hitters

library(caret)
set.seed(123)
# Define training control
set.seed(123) 
train.control <- trainControl(method = "cv", number = 10)
# Train the model
x2 <- train(Salary ~., data = x, method = "lm",
               trControl = train.control)
# Summarize the results
print(x)
x3 <- lm(Salary ~ poly(AtBat,3) + poly(Hits,3) + poly(Walks,3) + poly(CRuns,3) + poly(CWalks,3) + poly(PutOuts,3), data = x2)
summary(x3)
MSE = mean(x3$residuals^2)
print("Mean Squared Error: ")
print(MSE)

Answer 1

首先，如@dcarlson所述，您应该定义x。其次，x3不返回数据帧。如果您运行

str(x2)

您将看到lm函数中使用的所有元素都是称为trainingData的数据框的一部分。因此，如果您打算使用lm函数，请将其用作lm函数中的数据源，不 x2。我已经在下面重写了您的代码。

PS我距离R专家还很远，所以如果有人想用这个答案射击，那就继续吧，我总是愿意学习;）

attach(Hitters)
Hitters

library(caret)
set.seed(123)

# Define training control
set.seed(123) 
train.control <- trainControl(method = "cv", number = 10)

# Train the model
x2 <- train(Salary ~., data = x, method = "lm", trControl = train.control)

# Summarize the results
print(x2)
# str(x2) # $trainingData data.frame

x2$trainingData[["AtBat"]]
m <- x2$trainingData

x3 <- lm(Salary ~ poly(AtBat,3) + poly(Hits,3) + poly(Walks,3) + poly(CRuns,3) + poly(CWalks,3) + poly(PutOuts,3), data = m)
summary(x3)
MSE = mean(x3$residuals^2)
cat("Mean Squared Error: ", MSE) # use cat to concatenate text and variable value in one line

如何解决“无法将类强制转换为data.frame？

1 个答案: