common_errors.txt

Error in coxnet(x, is.sparse, ix, jx, y, weights, offset, alpha, nobs,  : 
  'list' object cannot be coerced to type 'double'

Error in glmnet(x, is.sparse, ix, jx, y, weights, offset, alpha, nobs,  : 
  'list' object cannot be coerced to type 'double'

Solution --> Input model matrix is not a matrix but a data frame, change it!


Error in if (min_reps > 0) { : missing value where TRUE/FALSE needed
In addition: There were 13 warnings (use warnings() to see them)
Solution --> Outcome/response variable is longer or shorter than number of rows of the model matrix or data. Happens when input is only a vector

 Error in { : 
  task 1 failed - "Non-numeric argument to mathematical function" 
When using for example SIBER, the input data (read counts) is not a matrix but a data.frame. Change to matrix and it should work.

 Error in cv.glmnet(x = model_matrix, y = response, family = family, alpha = alpha,  : 
  nfolds must be bigger than 3; nfolds=10 recommended 
- or any other related errors 
--> classification matrix OR classification vector/factor has no names!