glmnet Convergence for nth lambda value not reached after maxit=1000 iterations; solutions for larger lambdas returned

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I am using LASSO from glmnet-package to create predictions. Furthermore, I am using cv.glmnet-function to do 5-fold cross-validation to create Lasso.fit. This glmnet-object is then used in predict-function, with the rule of thumb s = "lambda.1se".

x <- scale(x)
x_test <- scale(x_test)

lasso.fit <- cv.glmnet(x,y, nfolds = 5, alpha=1, 
                  intercept =TRUE, standardize =TRUE, type.measure="mae")
lasso_pred <- predict(lasso.fit, x_test, s ="lambda.1se")

However, I am getting the following warning when running this model:

Warning: 
from glmnet Fortran code (error code -79); 
Convergence for 79th lambda value not reached after maxit=100000 iterations; 
solutions for larger lambdas returned

What does this warning mean?

Moreover, should I take this warning seriously, i.e., changing something the cv.glmnet-function?

Or is this something that I should not be that worried when creating predictions with penalized methods?

predict glmnet prediction r
1 Answers

By default, glmnet tries to compute the solutions for 100 lambda values. The error is saying that at the 79th lambda value, the max iteration (10^5 by default) of coordinate descent was hit. Therefore, since the solution did not meet the convergence criterion, only the solutions for the first 78 lambda values are given. You can still use cv.glmnet - it's just going to do model selection with those 78 lambdas. If you want to compute for more lambdas, simply pass another parameter maxit=... where ... is some number greater than 10^5.

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