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As far as I know glmnet does not calculate the standard errors of regression coefficients (since it fits model parameters using cyclic coordinate descent). So, if you need standardized regression coefficients, you will need to use some other method (e.g. glm)

Having said that, if the explanatory variables are standardized (which is the case in your code above since glmnet standardizes them by default) then the less important coefficients will be smaller than the more important ones - so you could rank them just by their magnitude. This becomes even more pronounced with non-trivial amount shrinkage (i.e. non-zero lambda)

Hope this helps..

As far as I know glmnet does not calculate the standard errors of regression coefficients (since it fits model parameters using cyclic coordinate descent). So, if you need standardized regression coefficients, you will need to use some other method (e.g. glm)

Having said that, if the explanatory variables are standardized before the fit and glmnet is called with "standardize=FALSE", then the less important coefficients will be smaller than the more important ones - so you could rank them just by their magnitude. This becomes even more pronounced with non-trivial amount shrinkage (i.e. non-zero lambda)

Hope this helps..

As far as I know glmnet does not calculate the standard errors of regression coefficients (since it fits model parameters using cyclic coordinate descent). So, if you need standardized regression coefficients, you will need to use some other method (e.g. glm)

Having said that, if IV's are standardized ( which is the case in your code above since glmnet standardizes them by default) then the less important coefficients will be smaller than the more important ones - so you could rank them just by their magnitude. This becomes even more pronounced with non-trivial amount shrinkage (i.e. non-zero lambda)

Hope this helps..

As far as I know glmnet does not calculate the standard errors of regression coefficients (since it fits model parameters using cyclic coordinate descent). So, if you need standardized regression coefficients, you will need to use some other method (e.g. glm)

Having said that, if the explanatory variables are standardized (which is the case in your code above since glmnet standardizes them by default) then the less important coefficients will be smaller than the more important ones - so you could rank them just by their magnitude. This becomes even more pronounced with non-trivial amount shrinkage (i.e. non-zero lambda)

Hope this helps..

As far as I know glmnet does not calculate the standard errors of regression coefficients (since it fits model parameters using cyclic coordinate descent). So, if you need standardized regression coefficients, you will need to use some other method (e.g. glm)

Having said that, if you are using shrinkage (i.e. a non-trivial value lambda) then the less important coefficients will be smaller than the more important ones - so you could rank them just by their magnitude (assuming that all IVs are standardized which is the case in your code above since glmnet standardizes them by default)

Hope this helps..

As far as I know glmnet does not calculate the standard errors of regression coefficients (since it fits model parameters using cyclic coordinate descent). So, if you need standardized regression coefficients, you will need to use some other method (e.g. glm)

Having said that, if IV's are standardized ( which is the case in your code above since glmnet standardizes them by default) then the less important coefficients will be smaller than the more important ones - so you could rank them just by their magnitude. This becomes even more pronounced with non-trivial amount shrinkage (i.e. non-zero lambda)

Hope this helps..