Ridge regression in R step by step

 

R Programming Code

library(glmnet)


#define predictor and response variables

y <- mtcars$hp

x <- data.matrix(mtcars[, c('mpg', 'wt', 'drat', 'qsec')])


#fit ridge regression model

model <- glmnet(x, y, alpha = 0)

summary(model)


#perform k-fold cross-validation to find optimal lambda value

cv_model <- cv.glmnet(x, y, alpha = 0)

plot(cv_model)


#find optimal lambda value that minimizes test MSE

best_lambda <- cv_model$lambda.min

best_lambda


#produce Ridge trace plot

plot(model, xvar = "lambda")


#find coefficients of best model

best_model <- glmnet(x, y, alpha = 0, lambda = best_lambda)

coef(best_model)


#calculate R-squared of model on training data

y_predicted <- predict(model, s = best_lambda, newx = x)


#find SST and SSE

sst <- sum((y - mean(y))^2)

sse <- sum((y_predicted - y)^2)


#find R-Squared

rsq <- 1 - sse / sst

rsq

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