> class(med.fit) [1] "lm"

根据你提供的代码和结果，`med.fit`对象的类别是`lm`，这意味着它是一个线性回归模型。通常，中介分析需要使用二元变量作为处理变量和中介变量，因此，确保你的数据框中的处理变量和中介变量都是二元变量。

如果你想进行中介分析，你需要确保数据框中包含名为"resilience"和"careeradaptability"的列，并且这两列都是二元变量。你可以使用以下代码检查列的类型：

class(m.data$resilience)
class(m.data$careeradaptability)

如果这两列不是二元变量，你可能需要对数据进行相应的预处理或转换，以确保它们满足中介分析的要求。

如果问题仍然存在，请提供更多的代码和数据细节，以便我能够更好地帮助你解决问题。

相关问题

Complete the Mint and Coin classes so that the coins created by a mint have the correct year and worth. - Each Mint instance has a year stamp. The update method sets the year stamp to the current_year class attribute of the Mint class. - The create method takes a subclass of Coin and returns an instance of that class stamped with the mint's year (which may be different from Mint.current_year if it has not been updated.) - A Coin's worth method returns the cents value of the coin plus one extra cent for each year of age beyond 50. A coin's age can be determined by subtracting the coin's year from the current_year class attribute of the Mint class. ```python class Mint: """A mint creates coins by stamping on years. The update method sets the mint's stamp to Mint.current_year. >>> mint = Mint() >>> mint.year 2020 >>> dime = mint.create(Dime) >>> dime.year 2020 >>> Mint.current_year = 2100 # Time passes >>> nickel = mint.create(Nickel) >>> nickel.year # The mint has not updated its stamp yet 2020 >>> nickel.worth() # 5 cents + (80 - 50 years) 35 >>> mint.update() # The mint's year is updated to 2100 >>> Mint.current_year = 2175 # More time passes >>> mint.create(Dime).worth() # 10 cents + (75 - 50 years) 35 >>> Mint().create(Dime).worth() # A new mint has the current year 10 >>> dime.worth() # 10 cents + (155 - 50 years) 115 >>> Dime.cents = 20 # Upgrade all dimes! >>> dime.worth() # 20 cents + (155 - 50 years) 125 """ current_year = 2020 def init(self): self.update() def create(self, kind): "*** YOUR CODE HERE " def update(self): " YOUR CODE HERE " class Coin: def init(self, year): self.year = year def worth(self): " YOUR CODE HERE ***" class Nickel(Coin): cents = 5 class Dime(Coin): cents = 10

Here is the completed code:

class Mint:
    """A mint creates coins by stamping on years.
    The update method sets the mint's stamp to Mint.current_year.
    """
    current_year = 2020
    
    def __init__(self):
        self.update()
        
    def create(self, kind):
        coin = kind(self.year)
        return coin
        
    def update(self):
        self.year = Mint.current_year
        
class Coin:
    def __init__(self, year):
        self.year = year
        
    def worth(self):
        age = Mint.current_year - self.year
        return self.cents + max(0, age - 50)
        
class Nickel(Coin):
    cents = 5
    
class Dime(Coin):
    cents = 10

Now we can create instances of the `Mint` class and use them to create coins. Here are some examples:

>>> mint = Mint()
>>> mint.year
2020

>>> dime = mint.create(Dime)
>>> dime.year
2020
>>> dime.worth()
10

>>> Mint.current_year = 2100
>>> nickel = mint.create(Nickel)
>>> nickel.year
2020
>>> nickel.worth()
35

>>> mint.update()
>>> Mint.current_year = 2175
>>> mint.create(Dime).worth()
35

>>> Mint().create(Dime).worth()
10

>>> dime.worth()
115

>>> Dime.cents = 20
>>> dime.worth()
125

As you can see, the `Mint` class correctly stamps coins with the current year and calculates their worth based on their age. The `Coin` class and its subclasses also correctly calculate the worth of a coin based on its age.

参考以下两段代码代码：第一段：# Lab5: Cross-Validation and the Bootstrap # The Validation Set Approach install.packages("ISLR") library(ISLR) set.seed(1) train=sample(392,196) lm.fit=lm(mpg~horsepower,data=Auto,subset=train) attach(Auto) mean((mpg-predict(lm.fit,Auto))[-train]^2) lm.fit2=lm(mpg~poly(horsepower,2),data=Auto,subset=train) mean((mpg-predict(lm.fit2,Auto))[-train]^2) lm.fit3=lm(mpg~poly(horsepower,3),data=Auto,subset=train) mean((mpg-predict(lm.fit3,Auto))[-train]^2) set.seed(2) train=sample(392,196) lm.fit=lm(mpg~horsepower,subset=train) mean((mpg-predict(lm.fit,Auto))[-train]^2) lm.fit2=lm(mpg~poly(horsepower,2),data=Auto,subset=train) mean((mpg-predict(lm.fit2,Auto))[-train]^2) lm.fit3=lm(mpg~poly(horsepower,3),data=Auto,subset=train) mean((mpg-predict(lm.fit3,Auto))[-train]^2) # Leave-One-Out Cross-Validation glm.fit=glm(mpg~horsepower,data=Auto) coef(glm.fit) lm.fit=lm(mpg~horsepower,data=Auto) coef(lm.fit) library(boot) glm.fit=glm(mpg~horsepower,data=Auto) cv.err=cv.glm(Auto,glm.fit) cv.err$delta cv.error=rep(0,5) for (i in 1:5){ glm.fit=glm(mpg~poly(horsepower,i),data=Auto) cv.error[i]=cv.glm(Auto,glm.fit)$delta[1] } cv.error第二段：library(caret) library(klaR) data(iris) splt=0.80 trainIndex <- createDataPartition(iris$Species,p=split,list=FALSE) data_train <- iris[ trainIndex,] data_test <- iris[-trainIndex,] model <- NaiveBayes(Species~.,data=data_train) x_test <- data_test[,1:4] y_test <- data_test[,5] predictions <- predict(model,x_test) confusionMatrix(predictions$class,y_test)。写出R代码完成以下任务：①建立50×30的随机数据和30个变量；②生成三组不同系数的①线性模型；③（线性回归中）分别计算这三组的CV值；④（岭回归中）分别画出这三组的两张图，两张图均以lambd为横坐标，一张图以CV error为纵坐标，一张图以Prediction error为纵坐标，两张图同分开在Plots位置，而且三组一组画完，再画另一组

以下是R代码完成以上任务：

①建立50×30的随机数据和30个变量：

set.seed(1)
X <- matrix(rnorm(50*30), nrow=50, ncol=30)
Y <- rnorm(50)

②生成三组不同系数的线性模型：

set.seed(2)
train <- sample(50, 25)
lm.fit1 <- lm(Y~X[,1]+X[,2], subset=train)
lm.fit2 <- lm(Y~X[,3]+X[,4]+X[,5], subset=train)
lm.fit3 <- lm(Y~X[,6]+X[,7]+X[,8]+X[,9], subset=train)

③（线性回归中）分别计算这三组的CV值：

library(boot)
cv.err1 <- cv.glm(Y~X[,1]+X[,2], data=data.frame(Y, X), K=10)$delta[1]
cv.err2 <- cv.glm(Y~X[,3]+X[,4]+X[,5], data=data.frame(Y, X), K=10)$delta[1]
cv.err3 <- cv.glm(Y~X[,6]+X[,7]+X[,8]+X[,9], data=data.frame(Y, X), K=10)$delta[1]

④（岭回归中）分别画出这三组的两张图，两张图均以lambd为横坐标，一张图以CV error为纵坐标，一张图以Prediction error为纵坐标，两张图同分开在Plots位置，而且三组一组画完，再画另一组：

library(glmnet)
X <- scale(X)
y <- scale(Y)
set.seed(3)
cv.fit1 <- cv.glmnet(X[train,], y[train], alpha=0, lambda=seq(0, 1, 0.01))
cv.fit2 <- cv.glmnet(X[train,], y[train], alpha=0, lambda=seq(0, 1, 0.01))
cv.fit3 <- cv.glmnet(X[train,], y[train], alpha=0, lambda=seq(0, 1, 0.01))

# Plot 1: CV error vs. lambda
plot(cv.fit1$lambda, cv.fit1$cvm, type='l', col='red', xlab='lambda', ylab='CV error')
lines(cv.fit2$lambda, cv.fit2$cvm, type='l', col='green')
lines(cv.fit3$lambda, cv.fit3$cvm, type='l', col='blue')
legend('topright', legend=c('Model 1', 'Model 2', 'Model 3'), col=c('red', 'green', 'blue'), lty=1)

# Plot 2: Prediction error vs. lambda
plot(cv.fit1$lambda, cv.fit1$glmnet.fit$dev.ratio[cv.fit1$lambda==cv.fit1$lambda.min], type='l', col='red', xlab='lambda', ylab='Prediction error')
lines(cv.fit2$lambda, cv.fit2$glmnet.fit$dev.ratio[cv.fit2$lambda==cv.fit2$lambda.min], type='l', col='green')
lines(cv.fit3$lambda, cv.fit3$glmnet.fit$dev.ratio[cv.fit3$lambda==cv.fit3$lambda.min], type='l', col='blue')
legend('bottomright', legend=c('Model 1', 'Model 2', 'Model 3'), col=c('red', 'green', 'blue'), lty=1)