README.md

In difuture-lmu/rmmodeldata: Remove data from models

rmmodeldata: Remove data from models

Overview

Building models is an essential skill for scientists or practioneers when working with data. In some research areas, such as it is for medical data, contains sensitive information which should not get shared. However, most R models, such as lm, contains the original data used for training the model.

This package provides mechanisms to train algorithms without storing the data or removing model data from a model. For simpler model objects (e.g. lm) it is sufficient to remove parts from the object to ensure privacy. More complex algorithms (e.g. partykit::cforest), on the other side, requires adaptions of the fitting and prediction algorithm to provide full privacy and functionality without storing data.

The functions you get by installing this package are: - A generic rmdata method for simpler models to just remove data - Functions to train and predict more complex models. Available algorithms: - cforest - traforest

Using a more complex function overwrites the original one and returns the same object with a suffix .nodat. For example, training a model using partykit::cforest returns an object of class cforest.nodat. For this class, special predict methods are implemented and used instead of the original partykit:::predict.cforest method.

Search for values (WIP)

In order to detect data in an R object, rmmodeldata provides a function searchObjectForValue which searches through an object for a specific value.

Disclaimer

• It is suggested to use the namespace rmmodeldata for calling functions to ensure the usage of the correct method. A message is printed when a method of rmmodeldata is used. Otherwise, it cannot be distinguished between the original and the new method.

• Searching through objects ignores functions and environments. Thus, if data are stored within an environment it is not detected.

Installation

Developer version:

remotes::install_github("difuture-lmu/rmmodeldata")

difuture-lmu/rmmodeldata documentation built on July 14, 2022, 4:49 a.m.