arXiv subjects

Adam Loy

Publications and source records attributed to Adam Loy.

5 recordsLinked to original sources

arXiv2021

Bootstrapping Clustered Data in R using lmeresampler

Linear mixed-effects models are commonly used to analyze clustered data structures. There are numerous packages to fit these models in R and conduct likelihood-based inference. The implementation of resampling-based procedures for inference are more limited. In this paper, we introduce the lmeresampler package for bootstrapping nested linear mixed-effects models fit via lme4 or nlme. Bootstrap estimation allows for bias correction, adjusted standard errors and confidence intervals for small samples sizes and when distributional assumptions break down. We will also illustrate how bootstrap resampling can be used to diagnose this model class. In addition, lmeresampler makes it easy to construct interval estimates of functions of model parameters.

stat.ME↗

arXiv2020

Bringing Visual Inference to the Classroom

In the classroom, we traditionally visualize inferential concepts using static graphics or interactive apps. For example, there is a long history of using apps to visualize sampling distributions. Recent developments in statistical graphics have created an opportunity to bring additional visualizations into the classroom to hone student understanding. Specifically, the lineup protocol for visual inference provides a framework for students see the difference between signal and noise by embedding a plot of observed data in a field of null (noise) plots. Lineups have proven valuable in visualizing randomization/permutation tests, diagnosing models, and even conducting valid inference when distributional assumptions break down. This paper provides an overview of how the lineup protocol for visual inference can be used to hone understanding of key statistical topics throughout the statistics curricula.

stat.OT↗

arXiv2016

Embracing Data Science

Statistics is running the risk of appearing irrelevant to today's undergraduate students. Today's undergraduate students are familiar with data science projects and they judge statistics against what they have seen. Statistics, especially at the introductory level, should take inspiration from data science so that the discipline is not seen as somehow lesser than data science. This article provides a brief overview of data science, outlines ideas for how introductory courses could take inspiration from data science, and provides a reference to materials for developing stand-alone data science courses.

stat.OT↗

arXiv2015

Variations of Q-Q Plots -- The Power of our Eyes!

In statistical modeling we strive to specify models that resemble data collected in studies or observed from processes. Consequently, distributional specification and parameter estimation are central to parametric models. Graphical procedures, such as the quantile-quantile (Q-Q) plot, are arguably the most widely used method of distributional assessment, though critics find their interpretation to be overly subjective. Formal goodness-of-fit tests are available and are quite powerful, but only indicate whether there is a lack of fit, not why there is lack of fit. In this paper we explore the use of the lineup protocol to inject rigor to graphical distributional assessment and compare its power to that of formal distributional tests. We find that lineups of standard Q-Q plots are more powerful than lineups of de-trended Q-Q plots and that lineup tests are more powerful than traditional tests of normality. While, we focus on diagnosing non-normality, our approach is general and can be directly extended to the assessment of other distributions.

stat.ME↗

arXiv2015

Model Choice and Diagnostics for Linear Mixed-Effects Models Using Statistics on Street Corners

The complexity of linear mixed-effects (LME) models means that traditional diagnostics are rendered less effective. This is due to a breakdown of asymptotic results, boundary issues, and visible patterns in residual plots that are introduced by the model fitting process. Some of these issues are well known and adjustments have been proposed. Working with LME models typically requires that the analyst keeps track of all the special circumstances that may arise. In this paper we illustrate a simpler but generally applicable approach to diagnosing LME models. We explain how to use new visual inference methods for these purposes. The approach provides a unified framework for diagnosing LME fits and for model selection. We illustrate the use of this approach on several commonly available data sets. A large-scale Amazon Turk study was used to validate the methods. R code is provided for the analyses.

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