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Title: Assessing selection stability in regularised regression models Authors:  Garth Tarr - University of Sydney (Australia) [presenting]
Abstract: The mplot R package provides an implementation of model stability and variable inclusion plots for researchers to use to better inform the variable selection process. The initial focus was on exhaustive searches through the model space, however, this quickly becomes infeasible for high dimensional models. An alternative approach for high dimensional models is to combine bootstrap model selection with regularisation procedures. There exists a number of fast and efficient method regularisation methods for variable selection in high dimensional regression settings. We have implemented variable inclusion plots and model stability plots using the glmnet package. We demonstrate the utility of the mplot package in identifying stable regularised model selection choices with respect to two main sources of uncertainty. Firstly, by resampling the data we are able to determine how often various models are chosen when the data changes. Secondly, we are able to evaluate how often competing models are chosen across a range of values for the tuning parameter. Exploring these two sources of uncertainty in model selection generates a large amount of raw data that needs to be processed. The mplot package provides a variety of methods to visualise this raw data to help inform a researcher's model selection choice.