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plsmselect (version 0.2.0)

Linear and Smooth Predictor Modelling with Penalisation and Variable Selection

Description

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).

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Version

Install

install.packages('plsmselect')

Monthly Downloads

163

Version

0.2.0

License

GPL-2

Maintainer

Indrayudh Ghosal

Last Published

November 24th, 2019

Functions in plsmselect (0.2.0)

nzeros

Internal Function
predict.gamlasso

Prediction from a fitted gamlasso model
readconfirm

Internal Function
print.gamlasso

Print a gamlasso object
gamlasso

Fitting a gamlasso model
summary.gamlasso

Summary for a gamlasso fit
simData

Simulated dataset to be used for gamlasso
cbh

Internal Function
meandist

Internal Function
formula_setup

Internal Function
gamlassoChecks

Checking data before fitting gamlasso
find_family

Internal Function
create_dataset

Function to create the simulated dataset
lasso_gam_loop

Internal Function
cumbasehaz

Cumulative Baseline Hazard of a gamlasso object
gamlassoFit

The function fitting a gamlasso model