Base case: input grouped data with a binomial outcome | Base case: input grouped survival data

Introduction to pretraining | Review of the lasso | Details of pretraining | ptLasso under the hood | Quick start | ptLasso uses the same syntax as glmnet | An example | Other details | Choosing $\alpha$, the pretraining parameter | Choosing $\lambda$, the lasso path parameter, for the first stage of pretraining | Fitting elasticnet or ridge models | Printing progress during model training | Using individual and overall models that were previously trained | Fitting the overall model without group-specific intercepts | Arguments for use in cv.glmnet | Parallelizing model fitting | More pretraining! | References

Intuition | Example

Example 1: covariates are constant over time | Example 2: covariates change over time

Introduction to pretraining | Review of the lasso | Details of pretraining | ptLasso under the hood | Quick start | ptLasso uses the same syntax as glmnet | An example | Other details | Choosing $\alpha$, the pretraining parameter | Choosing $\lambda$, the lasso path parameter, for the first stage of pretraining | Fitting elasticnet or ridge models | Printing progress during model training | Using individual and overall models that were previously trained | Fitting the overall model without group-specific intercepts | Arguments for use in cv.glmnet | Parallelizing model fitting | More pretraining! | References

Base case: input grouped data with a binomial outcome | Base case: input grouped survival data

Intuition | Example

Example 1: covariates are constant over time | Example 2: covariates change over time