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ridge.fit1 <- glmnet(x,y, alpha = 0)  
  
ridge.fitt <- coef(glmnet(x, y, alpha = 0, lambda = 3))
ridge.pred <- predict(ridge.fit1, s = 3, type = 'coefficients')

What's the difference between ridge.fitt and ridge.pred? What circumstances would I prefer to use one and not the other?

ridge.fit1 <- glmnet(x,y, alpha = 0)  
  
ridge.fitt <- coef(glmnet(x, y, alpha = 0, lambda = 3))
ridge.pred <- predict(ridge.fit1, s = 3, type = 'coefficients')

What's the difference between ridge.fitt and ridge.pred?

ridge.fit1 <- glmnet(x,y, alpha = 0)  
  
ridge.fitt <- coef(glmnet(x, y, alpha = 0, lambda = 3))
ridge.pred <- predict(ridge.fit1, s = 3, type = 'coefficients')

What's the difference between ridge.fitt and ridge.pred?

ridge.fit1 <- glmnet(x,y, alpha = 0)  
  
ridge.fitt <- coef(glmnet(x, y, alpha = 0, lambda = 3))
ridge.pred <- predict(ridge.fit1, s = 3, type = 'coefficients')

What's the difference between ridge.fitt and ridge.pred? What circumstances would I prefer to use one and not the other?

ridge.fit1 <- glmnet(x,y, alpha = 0)  
  
ridge.fitt <- glmnet(x, y, alpha = 0, lambda = 3)
ridge.pred <- predict(ridge.fit1, s = 3, type = 'coefficients')

What's the difference between ridge.fitt and ridge.pred?

ridge.fit1 <- glmnet(x,y, alpha = 0)  
  
ridge.fitt <- coef(glmnet(x, y, alpha = 0, lambda = 3))
ridge.pred <- predict(ridge.fit1, s = 3, type = 'coefficients')

What's the difference between ridge.fitt and ridge.pred?