get coef(cvfit, s = "lambda.min")

[aw2018]

how to simply get coef(cvfit, s = "lambda.min") as R does

[AsafManela]

I think the example in the README.md exactly that:

julia> cv = glmnetcv(X, y)
Least Squares GLMNet Cross Validation
55 models for 4 predictors in 10 folds
Best λ 0.343 (mean loss 76.946, std 12.546)

julia> argmin(cv.meanloss)
48

julia> cv.path.betas[:, 48]
4-element Array{Float64,1}:
 0.926911
 0.00366805
 0.0
 0.0

If not, perhaps look at other fields of the returned cv

[aw2018]

Thanks for response, but the question is which field should be? to get the coef automatically with lambda.min as R package does. R code shown in https://web.stanford.edu/~hastie/glmnet/glmnet_alpha.html

I think the example in the README.md exactly that:

julia> cv = glmnetcv(X, y)
Least Squares GLMNet Cross Validation
55 models for 4 predictors in 10 folds
Best λ 0.343 (mean loss 76.946, std 12.546)

julia> argmin(cv.meanloss)
48

julia> cv.path.betas[:, 48]
4-element Array{Float64,1}:
 0.926911
 0.00366805
 0.0
 0.0

If not, perhaps look at other fields of the returned cv

[AsafManela]

I would guess its cv.meanloss but you could try running the same example data in both R and GLMNet.jl and compare

[aw2018]

cv.meanloss should be a series of mean loss for a certain λ as shown below. what I need a coef matrix to select variables for a small s.

cv = glmnetcv(X, y)
Least Squares GLMNet Cross Validation
55 models for 4 predictors in 10 folds
Best λ 0.241 (mean loss 112.897, std 16.404)

julia> cv.meanloss
55-element Array{Float64,1}:
877.2810878855138
761.7422582965088
650.8555199966314
558.8059633274238
482.39442752396894
418.96499646821854
366.3127578869651
322.60722407531716
286.3287550353564
256.2157763950583
231.22095929491542
210.47484061338398
193.33085107722835
⋮
112.96750068583216
112.94587149513234
112.92697317790956
112.91614261629186
112.90772816957528
112.90267191956609
112.90089098278163
112.89809237633227
112.89745539845951
112.8982327601517
112.89995121762695
112.90229334644275

[AsafManela]

That is what the other two lines I quoted earlier do.
argmin(cv.meanloss) gives the index of the path segment with the lowest mse (in the example 48).
cv.path.betas[:, argmin(cv.meanloss)] gives the coefficient vector at that segment.

[aw2018]

Thanks