Linear and Smooth Predictor Modelling with Penalisation and Variable Selection
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Function to create the simulated dataset
Cumulative Baseline Hazard of a gamlasso object
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Fitting a gamlasso model
Checking data before fitting gamlasso
The function fitting a gamlasso model
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Prediction from a fitted gamlasso model
Print a gamlasso object
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Summary for a gamlasso fit
Fit a model with potentially many linear and smooth predictors. Interaction effects can also be quantified. Variable selection is done using penalisation. For l1-type penalties we use iterative steps alternating between using linear predictors (lasso) and smooth predictors (generalised additive model).