glmnet 未从 cv.glmnet 收敛 lambda.min

我跑了20倍cv.glmnet套索模型以获得 lambda 的“最佳”值。但是，当我尝试重现结果时glmnet()，我收到一个错误，内容如下：

Warning messages:
1: from glmnet Fortran code (error code -1); Convergence for 1th lambda
   value not reached after maxit=100000 iterations; solutions for larger 
   lambdas returned 
2: In getcoef(fit, nvars, nx, vnames) :
   an empty model has been returned; probably a convergence issue

我的代码是这样写的：

set.seed(5)
cv.out <- cv.glmnet(x[train,],y[train],family="binomial",nfolds=20,alpha=1,parallel=TRUE)
coef(cv.out)
bestlam <- cv.out$lambda.min
lasso.mod.best <- glmnet(x[train,],y[train],alpha=1,family="binomial",lambda=bestlam)

现在，价值bestlam上面是2.976023e-05所以也许这就是造成问题的原因？这是 lambda 值的舍入问题吗？有什么原因导致我无法直接从glmnet()功能？如果我使用与该值相似范围内的 lambda 值向量bestlam，我没有任何问题。

你将一个 lambda 传递给你的glmnet (lambda=bestlab）这是一个很大的禁忌（您正在尝试仅使用一个 lambda 值来训练模型）。

来自glmnet文档(?glmnet):

lambda: A user supplied lambda sequence. Typical usage is to have the 
program compute its own lambda sequence based on nlambda and 
lambda.min.ratio. Supplying a value of lambda overrides this. WARNING: use 
with care. Do not supply a single value for lambda (for predictions after CV 
use predict() instead). Supply instead a decreasing sequence of lambda 
values. glmnet relies on its warms starts for speed, and its often faster to 
fit a whole path than compute a single fit.