Stephan J Ritter and Nicholas P Jewell and Alan E Hubbard
We describe the R package multiPIM, including statistical background, functionality and user options. The package is for variable importance analysis, and is meant primarily for analyzing data from exploratory epidemiological studies, though it could certainly be applied in other areas as well. The approach taken to variable importance comes from the causal inference field, and is different from approaches taken in other R packages. By default, multiPIM uses a double robust targeted maximum likelihood estimator (TMLE) of a parameter akin to the attributable risk. Several regression methods/machine learning algorithms are available for estimating the nuisance parameters of the models, including super learner, a meta-learner which combines several different algorithms into one. We describe a simulation in which the double robust TMLE is compared to the graphical computation estimator. We also provide example analyses using two data sets which are included with the package.
The most recent version of the multiPIM package is available in CRAN, Package multiPIMArticle
Stephan J Ritter and Nicholas P Jewell and Alan E Hubbard, et al. 2014. "R Package multiPIM: A Causal Inference Approach to Variable Importance Analysis." Journal of Statistical Software. 57 (8) Retrieved 06/24/2019 from researchcompendia.org/compendia/2014.471/
Compendium Type: Software Primary Research Field: Statistics Content License: Other (must be approved by ResearchCompendia Code License: Other (must be approved by ResearchCompendia
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