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I am using glmnet for LASSO. My data set contains several continuous variables and one categorical variable (it has four levels). I wondered if I could treat three dummy variables as other continuous variables. Should I use a type of group LASSO approach for the three dummies?

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    $\begingroup$ Normally, yes, you keep your factors all together. There's several R packages that can do this, including glmnet. $\endgroup$
    – Glen_b
    Commented Sep 10, 2014 at 0:49
  • $\begingroup$ @Glen_b What are the options in glmnet for running group lasso with categorical variables? I don't see anything about categorical variables at web.stanford.edu/~hastie/glmnet/glmnet_alpha.html or cran.r-project.org/web/packages/glmnet/glmnet.pdf $\endgroup$
    – Adrian
    Commented Feb 22, 2017 at 16:57
  • $\begingroup$ See the type.multinomial argument to glmnet $\endgroup$
    – Glen_b
    Commented Feb 22, 2017 at 22:54
  • $\begingroup$ perhaps leave dummies not penalized (as what you with intercept), unless you have a good reason to put a constraint on them. if so, just add dummies and impute zero penalty weights if you use glmnet implementation. $\endgroup$
    – Jonas Striaukas
    Commented Mar 25, 2020 at 23:43

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As far as I am aware glmnet doesn't have this feature implemented yet. @Glen_b's suggestion of using type.multinomial is used to group variables across all responses in a multinomial model, but there's no way of grouping independent variables in a model. see

https://cran.r-project.org/web/packages/grplasso/grplasso.pdf

for an alternative.

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