[R] Different Lambdas and Coefficients between cv.glmnet and intercept = FALSE

Bert Gunter bgunter@4567 @end|ng |rom gm@||@com
Tue Feb 23 23:14:22 CET 2021

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On Tue, Feb 23, 2021 at 1:07 PM <kevinegan31 using gmail.com> wrote:

> Hello,
> I'm currently reviewing how to correctly implement `glmnet` and am having
> a hard time understanding why the results seem to be different between each
> method when `intercept = TRUE/FALSE` as I thought it should just drop the
> intercept from the model. However, it seems to be acting a bit different
> and I'm not sure how.
> For a given lambda, if both `X` and `y` are scaled, it appears we can
> identify the same results:
> ```
> library(glmnet)
> data(QuickStartExample)
> lambda_grid <- 10 ^ seq(10, -2, length = 100)
> With_Intercept<-glmnet(scale(x),c(scale(y)))
> Without_Intercept<-glmnet(scale(x),c(scale(y)), intercept=FALSE)
> # Extract coefficients at a single value of lambda
> cbind(coef(With_Intercept,s=0.01), coef(Without_Intercept,s=0.01))[-1,]
> ```
> While this is good, it's not clear to me how to put these back into their
> original scale. Further, this is for a given value of lambda. When using
> `cv.glmnet`, I'd like to identify the optimal lambda such that:
> ```
> With_Intercept <- cv.glmnet(scale(x),c(scale(y)), lambda = lambda_grid)
> Without_Intercept <- cv.glmnet(scale(x), c(scale(y)), lambda =
> lambda_grid, intercept=FALSE)
> cbind(coef(With_Intercept, s=With_Intercept$lambda.min, exact = TRUE, x =
> scale(x), y = scale(y)),
>       coef(Without_Intercept, s=Without_Intercept$lambda.min, exact =
> TRUE, x = scale(x), y = scale(y)))[-1,]
> ```
> If I use `With_Intercept$lambda.min` to identify the `Without_Intercept`
> model, I get the same coefficients, but this doesn't necessarily give me
> confidence in what is the right model to use. Further, I'm not sure how to
> put the coefficients back into the right scale.
> I've tried to compare all of the possible combinations between
> standardising, scaling, and leaving the variables as they are, but I'm
> still struggling with the best method and how to ensure I'm implementing
> `glmnet` correctly.
> If anyone has advice on how to proceed and interpret these methods or get
> consistent results I would appreciate it. I've been reading the
> Introduction to Statistical Learning, Elements of Statistical Learning,
> Statistical Learning and Sparsity, as well as the `glmnet` vignette but am
> still a bit unclear.
> Thanks,
> Kevin
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