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Can you use glmnet to do Lasso regression?
I am starting to dabble with the use of glmnet with LASSO Regression where my outcome of interest is dichotomous. I have created a small mock data frame below:
How are categorical variables transformed into factors in glmnet?
Categorical variables are usually first transformed into factors, then a dummy variable matrix of predictors is created and along with the continuous predictors, is passed to the model. Keep in mind, glmnet uses both ridge and lasso penalties, but can be set to either alone.
How to do regression with effect coding instead of dummy?
My dependent variable is a logit transformed ratio. It is fairly easy just to run a normal regression in R, as R automatically know how to code dummies as soon as they are of the type “factor”. However this type of coding also implies that one category from each variable is used as a baseline, making it hard to interpret.
Can a lasso be used with factor variables?
I know that having factor variables doesn’t really work in LASSO through either lars or glmnet, but the variables are too many and there are too many different, unordered values they can take on to reasonably recode them numerically. Can LASSO be used in this situation? How do I do this?
Why are the predictions from Poisson Lasso regression?
In the help for the predict function it states that specifying type = “link” gives “the linear predictors” for poisson models, whereas specifying type = “response” gives the “fitted mean” for poisson models (in the case of my dumb example it generates 500 values of 2.988 ).
How to fit regularized Cox regression to glmnet?
“Regularized Cox Regression” describes how to fit regularized Cox models for survival data with glmnet. “GLM family functions in glmnet ” describes how to fit custom generalized linear models (GLMs) with the elastic net penalty via the family argument.
How does glmnet return a sequence of models?
The function glmnet returns a sequence of models for the users to choose from. In many cases, users may prefer the software to select one of them.
Which is the best example of Lasso regression?
I have created a small mock data frame below: The goal of this example is to make use of LASSO to create a model predicting child asthma status from the list of 6 potential predictor variables ( age, gender, bmi_p, m_edu, p_edu, and f_color ).
What are the special values of λ in glmnet?
Two special values along the λ sequence are indicated by the vertical dotted lines. lambda.min is the value of λ that gives minimum mean cross-validated error, while lambda.1se is the value of λ that gives the most regularized model such that the cross-validated error is within one standard error of the minimum.