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IPFStructPenalty

IPFStructPenalty fits the Lasso, elastic and tree-lasso methods with penalty factors for different data sources, and uses the Efficient Parameter Selection via Global Optimization (EPSGO) algorithm to optimize multiple penalty parameters.

R package instruction

You can download the package and install it locally, or install it from github directly by the code:

library(devtools)
devtools::install_github("zhizuio/IPFStructPenalty/IPFStructPenalty")

See the file test.R for s simple start with simulated data. The scripts Sim_reg.R and GDSC_reg.R are for simulation study and analyzing real data, respectively, presented in Zhao & Zucknick (2020). The original runnings were on HPC. They will be every slow to be run on PC.

Reference

Zhao Z, Zucknick M (2020). Structured penalized regression for drug sensitivity prediction. Journal of the Royal Statistical Society: Series C (Applied Statistics), 69(3): 525-545, URL: https://doi.org/10.1111/rssc.12400.

Update

New in version IPFStructPenalty_1.0.2.tar.gz (15 November 2019):

Fixed the specified directory of the installed package (lib.loc) for the parallelization.

version IPFStructPenalty_1.0.1.tar.gz (30 August 2019):

The unpenalized argument for the conditional logistic lasso has been added and passed from the EPSGO algorithm.

Version IPFStructPenalty_1.0.tar.gz (23 August 2019):

The package and all codes have been adapted to do all data analysis in Zhao & Zucknick (2020).

Version IPFStructPenalty_0.1.4.tar.gz (21 August 2019):

The parallelisation issue of the conditional logistic lasso have been fixed.

Version IPFStructPenalty_0.1.3.tar.gz (15 August 2019):

Some bugs of the conditional logistic lasso have been fixed.

Version IPFStructPenalty_0.1.2.tar.gz (02 August 2019):

The help files of functions have been updated.

Version IPFStructPenalty_0.1.1.tar.gz (01 August 2019):

1. The DESCRIPTION file has been updated.

2. The conditional logistic lasso has been added, but using the equivalent Cox lasso model because the stratification factor.