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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 function for cross validation in glmnet?
Cross-validation is perhaps the simplest and most widely used method for that task. cv.glmnet is the main function to do cross-validation here, along with various supporting methods such as plotting and prediction. cvfit <- cv.glmnet (x, y) cv.glmnet returns a cv.glmnet object, a list with all the ingredients of the cross-validated fit.
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\\))
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 is the regularization path computed in glmnet?
Glmnet is a package that fits generalized linear and similar models via penalized maximum likelihood. The regularization path is computed for the lasso or elastic net penalty at a grid of values (on the log scale) for the regularization parameter lambda. The algorithm is extremely fast, and can exploit sparsity in the input matrix x.
Which is the best way to install glmnet?
The core of glmnet is a set of fortran subroutines, which make for very fast execution. The package also includes methods for prediction and plotting, and a function that performs K-fold cross-validation. Like many other R packages, the simplest way to obtain glmnet is to install it directly from CRAN.