Abstract
The mplot package provides an easy to use implementation of model stability and variable inclusion plots (M\"uller and Welsh 2010; Murray, Heritier, and M\"uller 2013) as well as the adaptive fence (Jiang, Rao, Gu, and Nguyen 2008; Jiang, Nguyen, and Rao 2009) for linear and generalised linear models. We provide a number of innovations on the standard procedures and address many practical implementation issues including the addition of redundant variables, interactive visualisations and approximating logistic models with linear models. An option is provided that combines our bootstrap approach with glmnet for higher dimensional models. The plots and graphical user interface leverage state of the art web technologies to facilitate interaction with the results. The speed of implementation comes from the leaps package and cross-platform multicore support.
Highlights
The mplot package provides an easy to use implementation of model stability and variable inclusion plots (Muller and Welsh 2010; Murray, Heritier, and Muller 2013) as well as the adaptive fence (Jiang, Rao, Gu, and Nguyen 2008; Jiang, Nguyen, and Rao 2009) for linear and generalised linear models
In this article we introduce the mplot package in R, which provides a suite of interactive visualisations and model summary statistics for researchers to use to better inform the variable selection process (Tarr, Muller, and Welsh 2016; R Core Team 2015)
The implementation we provide in the mplot package is inspired by the simplified adaptive fence proposed by Jiang et al (2009), which represents a significant advance over the original fence method proposed by Jiang et al (2008)
Summary
In this article we introduce the mplot package in R, which provides a suite of interactive visualisations and model summary statistics for researchers to use to better inform the variable selection process (Tarr, Muller, and Welsh 2016; R Core Team 2015). The mplot package currently implements “variable inclusion plots”, “model stability plots” and a model selection procedure inspired by the adaptive fence of Jiang et al (2008).
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