R glmnet不从cv.glmnet为lambda.min收敛

我运行了一个20倍的

cv.glmnet

lasso模型来获得lambda的“最佳”值。但是，当我尝试从

glmnet（）

复制结果时，我会收到一个错误，错误如下：

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)

set.seed（5）
cv.outglmnet在这方面有点棘手-您需要使用一系列lambda（例如，设置nlambda=101）运行您的最佳模型，然后当您预测set
s=bestlam
和
exact=FALSE
时，您将一个lambda传递给您的
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）

从
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.