The VGAM Package for Categorical Data Analysis

Classical categorical regression models such as the multinomial logit and proportional odds models are shown to be readily handled by the vector generalized linear and additive model (VGLM/VGAM) framework. Additionally, there are natural extensions, such as reduced-rank VGLMs for dimension reduction, and allowing covariates that have values specific to each linear/additive predictor, e.g., for consumer choice modeling. This article describes some of the framework behind the VGAM R package, its usage and implementation details.

Seeing the light – finding the poetic content of design objects

This paper presents the process and initial results of a research through design project attempting to understand the poetic qualities of design objects. This exploration forms part of a PhD study addressing design artefacts as poetic objects - objects that both embed and conjure memory, association and imagination. The research examines the ways in which design objects can be poetic and how designers actively and knowingly use objects to poetic effect. It is proposed that the poetic content of design artefacts can be located on a continuum ranging from the experiential - relating to how we perceive things - to the reflective and cultural. What unites these levels is the capacity of design objects to reveal and change our way of looking at things. The practice uses the design of lighting as a vehicle for exploring the poetic meaning of designed objects more generally. Starting with the notion that lights do more than provide light, the current phase of practice examines the ways in which luminaires can mediate how we perceive and experience light and explores, in particular, the more nuanced and ephemeral qualities of light that escape conscious attention

Bayesian nonparametric Plackett-Luce models for the analysis of preferences for college degree programmes

In this paper we propose a Bayesian nonparametric model for clustering partial ranking data. We start by developing a Bayesian nonparametric extension of the popular Plackett-Luce choice model that can handle an infinite number of choice items. Our framework is based on the theory of random atomic measures, with the prior specified by a completely random measure. We characterise the posterior distribution given data, and derive a simple and effective Gibbs sampler for posterior simulation. We then develop a Dirichlet process mixture extension of our model and apply it to investigate the clustering of preferences for college degree programmes amongst Irish secondary school graduates. The existence of clusters of applicants who have similar preferences for degree programmes is established and we determine that subject matter and geographical location of the third level institution characterise these clusters.Comment: Published in at http://dx.doi.org/10.1214/14-AOAS717 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

The Hankie Probe: A Materialistic Approach to Mobile UX Research

Mobile user experience (UX) research can benefit from unexplored opportunities from theory and practice. Contemporary sociology has developed sophisticated understandings of mobilities that can expand the scope of mobile HCI research. At the same time, we need to extend the scope of mobile experience beyond its current main foci on the portable device and moments of experience. We report the interim results of exploratory pilot studies of a fabric based probe that has been developed to extend the scope of mobile experience research both theoretically and in the range of insights that can be collected in mobile user studies. We report our initial experiences with a 'hankie' (handkerchief) probe that aims to gather rich usage and experience insights for early stages of design

Formal Properties as the Basis for Value in Music

This paper defends the thesis that value in a piece of music is based in its formal properties rather than its non-formal properties. Two arguments are presented to support this conclusion. The first argument shows that if value in music is to be objective, then it must be grounded in a piece\u27s formal properties rather than its non-formal properties. In the second argument, a number of alternate possibilities for grounding value in music are considered and shown to miss the mark or be inadequate. Finally, a number of possible objections against the arguments and conclusion are considered and possible responses to them given

On the Hauck-Donner Effect in Wald Tests: Detection, Tipping Points, and Parameter Space Characterization

The Wald test remains ubiquitous in statistical practice despite shortcomings such as its inaccuracy in small samples and lack of invariance under reparameterization. This paper develops on another but lesser-known shortcoming called the Hauck--Donner effect (HDE) whereby a Wald test statistic is not monotonely increasing as a function of increasing distance between the parameter estimate and the null value. Resulting in an upward biased $p$-value and loss of power, the aberration can lead to very damaging consequences such as in variable selection. The HDE afflicts many types of regression models and corresponds to estimates near the boundary of the parameter space. This article presents several new results, and its main contributions are to (i) propose a very general test for detecting the HDE, regardless of its underlying cause; (ii) fundamentally characterize the HDE by pairwise ratios of Wald and Rao score and likelihood ratio test statistics for 1-parameter distributions; (iii) show that the parameter space may be partitioned into an interior encased by 5 HDE severity measures (faint, weak, moderate, strong, extreme); (iv) prove that a necessary condition for the HDE in a 2 by 2 table is a log odds ratio of at least 2; (v) give some practical guidelines about HDE-free hypothesis testing. Overall, practical post-fit tests can now be conducted potentially to any model estimated by iteratively reweighted least squares, such as the generalized linear model (GLM) and Vector GLM (VGLM) classes, the latter which encompasses many popular regression models.Comment: 6 figure