Glmnet - Introduction
Here is a brief introduction of the package. For more details and examples, use help glmnet or help cvglmnet in Matlab.
Suppose is the input matrix and the response vector. For the Gaussian family, glmnet solves the penalized residual sum of squares,
where is a complexity parameter and is a compromise between ridge and lasso. Note that it becomes the lasso when and the ridge regression when .
For other families, glmnet maximizes the appropriate penalized log-likelihood (partial likelihood for the cox model), or minimize the penalized negative one. Take the binomial model for example, it solves
The algorithm uses cyclical coordinate descent in a pathwise fashion. In addition to basic settings, many more options are available: observation weights, choice of lambda sequence, grouping, etc. For more information, see the reference papers, help file or the documentation (in progress).
Two central functions of the package are:
We give a simple example here just to point the way. More exploration can be done by referring to the help files or the illustrative documentation.
Suppose x is the input matrix and y the response vector. Then,
List of Major Functions