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Under the context of clinical research, it sometimes makes sense to have some "must-keep" variables for Cox regressions. This is beyond the standard penalized linear models supported in msaenet and obviously not officially supported by glmnet or ncvreg.
As a workaround, we can add a new argument penalty.factor.init which will be assigned as the penalty.factor in the first estimation step (and first estimation step only). Users can assign a lower penalty factor for must-keep variables and a higher penalty factor for the other variables. The factor sizes will be subjective, though.
The text was updated successfully, but these errors were encountered:
Under the context of clinical research, it sometimes makes sense to have some "must-keep" variables for Cox regressions. This is beyond the standard penalized linear models supported in msaenet and obviously not officially supported by glmnet or ncvreg.
As a workaround, we can add a new argument
penalty.factor.init
which will be assigned as thepenalty.factor
in the first estimation step (and first estimation step only). Users can assign a lower penalty factor for must-keep variables and a higher penalty factor for the other variables. The factor sizes will be subjective, though.The text was updated successfully, but these errors were encountered: