LASSO回归R语言代码
时间: 2023-07-10 17:05:14 浏览: 68
以下是使用R语言进行LASSO回归的代码示例:
```
# 加载LASSO包
library(glmnet)
# 准备数据
data(mtcars)
y <- mtcars$mpg
x <- model.matrix(mpg ~ ., data = mtcars)[,-1]
# 设置alpha值
alpha <- 1 # LASSO回归
# 进行LASSO回
fit <- glmnet(x, y, alpha = alpha)
# 输出结果
print(fit)
# 绘系数路径图
plot(fit, xvar = "lambda", label = TRUE)
```
在这个例子中,我们使用了mtcars数据集,并将mpg作为因变量,其他变量作为自变量。我们使用model.matrix函数将数据转换为矩阵形式,并删除第一列(因为它是常数项)。然后,我们设置alpha值为1,表示我们要进行LASSO回归。我们使用glmnet函数进行回归,并将结果存储在fit对象中。最后,我们使用plot函数绘制系数路径图。
相关问题
lasso回归R语言代码
Sure! Here's an example of Lasso regression code in R:
```R
# Load the glmnet package
library(glmnet)
# Generate some sample data
set.seed(123)
x <- matrix(rnorm(1000), ncol = 10)
y <- rnorm(100)
# Split the data into training and testing sets
train_idx <- sample(1:100, 70)
x_train <- x[train_idx, ]
y_train <- y[train_idx]
x_test <- x[-train_idx, ]
y_test <- y[-train_idx]
# Fit the Lasso regression model using cross-validation
fit <- cv.glmnet(x_train, y_train, alpha = 1)
# Determine the optimal lambda value
best_lambda <- fit$lambda.min
# Print the optimal lambda value
print(best_lambda)
# Fit the Lasso regression model with the optimal lambda value
lasso_model <- glmnet(x_train, y_train, alpha = 1, lambda = best_lambda)
# Predict on the test set
predictions <- predict(lasso_model, newx = x_test)
# Calculate the root mean squared error (RMSE)
rmse <- sqrt(mean((predictions - y_test)^2))
# Print the RMSE
print(rmse)
```
This code uses the `glmnet` package to perform Lasso regression. It starts by generating some sample data, splitting it into training and testing sets. Then, it fits the Lasso regression model using cross-validation and selects the optimal lambda value. Finally, it fits the Lasso model with the optimal lambda value, predicts on the test set, and calculates the root mean squared error (RMSE).
r语言lasso回归分析代码
下面是一个简单的 R 语言 Lasso 回归分析的示例代码:
```r
# 导入 Lasso 包
library(glmnet)
# 生成示例数据
set.seed(123)
n <- 100
p <- 20
x <- matrix(rnorm(n*p), ncol=p)
y <- x[,1] + 2*x[,2] + rnorm(n)
# 将数据分为训练集和测试集
train.index <- sample(1:n, n*0.7)
x.train <- x[train.index, ]
y.train <- y[train.index]
x.test <- x[-train.index, ]
y.test <- y[-train.index]
# 使用 Lasso 回归进行建模
fit <- glmnet(x.train, y.train, alpha=1)
# 使用测试集评估模型
y.pred <- predict(fit, newx=x.test)
mse <- mean((y.test - y.pred)^2)
print(paste0("测试集均方误差:", mse))
```
在这个示例中,我们首先导入 Lasso 包,然后生成一个带有 20 个特征的示例数据集。我们将数据集分为训练集和测试集,并使用 `glmnet` 函数进行 Lasso 回归建模。最后,我们使用测试集评估模型的性能。