What is Glmnet package?
Glmnet is a package that fits generalized linear and similar models via penalized maximum likelihood. It fits linear, logistic and multinomial, poisson, and Cox regression models. It can also fit multi-response linear regression, generalized linear models for custom families, and relaxed lasso regression models.
Can you use categorical variables in Lasso?
This allows you to deal with multiple categorical variables without identifiability problems. In answer to your specific questions: (1) LASSO is an estimation method for the coefficients, but the coefficients themselves are defined by the initial model equation for your regression.
What kind of regression models can glmnet fit?
The algorithm is extremely fast, and can exploit sparsity in the input matrix x. It fits linear, logistic and multinomial, poisson, and Cox regression models. It can also fit multi-response linear regression, generalized linear models for custom families, and relaxed lasso regression models.
How are GLM family functions used in glmnet?
“GLM family functions in glmnet ” describes how to fit custom generalized linear models (GLMs) with the elastic net penalty via the family argument. “The Relaxed Lasso” describes how to fit relaxed lasso regression models using the relax argument. over a grid of values of λ covering the entire range of possible solutions.
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.
How to make predictions from the fitted glmnet object?
Users can make predictions from the fitted glmnet object. In addition to the arguments in coef, the primary argument is newx, a matrix of new values for x at which predictions are desired. The type argument allows users to choose the type of prediction returned: “link” returns the fitted values (i.e. \\(\\hat\\beta_0 + x_i^T\\hat\\beta\\))