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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