Comments (2)
Note that this only happens if number of features >= number observations -1
see
using Lasso
X = rand(20,8)
y = rand(20)
# this is to run this step: https://github.com/JuliaStats/Lasso.jl/blob/9d099f512afb6042b1094714e0ab280cf10f3863/src/segselect.jl#L276
# I set λ to a single variable to simplify things
path = fit(LassoPath, X,y; λ=[0.005])
# select model to replicate this step: https://github.com/JuliaStats/Lasso.jl/blob/9d099f512afb6042b1094714e0ab280cf10f3863/src/segselect.jl#L278
m1 = selectmodel(path, MinAICc());
show(m1)
m2 = selectmodel(path, MinAICc());
show(m2)
show(m1)
works completely fine
from lasso.jl.
Calculating standard errors for a lasso selected model are not a resolved problem AFAIK.
I think show
fails the first time because the standard errors are all 0
. You can check by running stderror(m1)
.
That it works on subsequent tries is a bug, that can be fixed by deep copying m.rr
used in
Line 169 in 9d099f5
instead of changing it with each call.
Is there a way to make
show
display something sensible without taking a stand on standard errors?from lasso.jl.
Related Issues (20)
- Feature requests HOT 1
- compats are outdated
- Intermittent Bounds Error HOT 3
- Accept weights that are not `AbstractVector{<:AbstractFloat}` HOT 3
- possible test failure in upcoming Julia version 1.5
- Fitting a LassoModel with a particular choice of lambda HOT 9
- Package fails when response is zero vector.
- Bug: BoundsError: attempt to access 200-element Array{Float64,1} at index [201] HOT 2
- TagBot trigger issue HOT 6
- Bug: Bounds error thrown for small regularization parameters
- please tag a new release for Pkg resolver HOT 5
- 0.6.3 precompilation error
- upgrades and documentation HOT 1
- preferred way of saving model HOT 1
- Document maximum iterations settings. HOT 1
- Declare error exception or throw warning instead of just using error()
- Model type LassoModel doesn't support intercept HOT 1
- StackOverflow with type Vector{Union{Float64, Missing}} even if no missing values present
- matrix is not square,how to fix it? HOT 2
- Divergence from GLMnet when using a matrix with many variables HOT 2
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from lasso.jl.