<b>prefmod</b>: An<i>R</i>Package for Modeling Preferences Based on Paired Comparisons, Rankings, or Ratings

Hatzinger, Reinhold; Dittrich, Regina

doi:10.18637/jss.v048.i10

Cited by 80 publications

(81 citation statements)

References 19 publications

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“…To investigate Hypothesis 1, we used loglinear BradleyTerry (LLBT) modeling [12] of the 6060 pairwise aesthetic preference comparisons to produce ranked "worth" scores for each of the four schematization styles. The worth score allows the consistency of preference to be assessed in forming an overall ranking of the four classes.…”

Section: Resultsmentioning

confidence: 99%

Exploring Curved Schematization of Territorial Outlines

Goethem

Meulemans

Speckmann

et al. 2015

IEEE Trans. Visual. Comput. Graphics

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Abstract-Hand-drawn schematized maps traditionally make extensive use of curves. However, there are few automated approaches for curved schematization; most previous work focusses on straight lines. We present a new algorithm for areapreserving curved schematization of geographic outlines. Our algorithm converts a simple polygon into a schematic crossing-free representation using circular arcs. We use two basic operations to iteratively replace consecutive arcs until the desired complexity is reached. Our results are not restricted to arcs ending at input vertices. The method can be steered towards different degrees of "curviness": we can encourage or discourage the use of arcs with a large central angle via a single parameter. Our method creates visually pleasing results even for very low output complexities. To evaluate the effectiveness of our design choices we perform a geometric evaluation of the resulting schematizations. Besides the geometric qualities of our algorithm, we also investigate the potential of curved schematization as a concept. We conducted an online user study investigating the effectiveness of curved schematizations compared to straight-line schematizations. While the visual complexity of curved shapes was judged higher than that of straight-line shapes, users generally preferred curved schematizations. We observed that curves significantly improved the ability of users to match schematized shapes of moderate complexity to their unschematized equivalents.

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Section: Resultsmentioning

confidence: 99%

Exploring Curved Schematization of Territorial Outlines

Goethem

Meulemans

Speckmann

et al. 2015

IEEE Trans. Visual. Comput. Graphics

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“…The model estimation for ordinal BTL models were computed with the ordBTL package (Casalicchio, 2013), which is implemented in the statistical software R (R Core Team, 2013) and is able to fit models with any number of response categories. Other packages for model estimation, which can handle responses up to 3 categories but have no built-in variable selection procedure, are prefmod (Hatzinger & Dittrich, 2012) and BradleyTerry2 (Turner & Firth, 2012).…”

Section: Discussionmentioning

confidence: 99%

Subject-specific Bradley–Terry–Luce models with implicit variable selection

Casalicchio

Tutz

Schauberger

2015

Statistical Modelling

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The Bradley-Terry-Luce (BTL) model for paired comparison data is able to obtain a ranking of the objects that are compared pairwise by subjects. The task of each subject is to make preference decisions in favor of one of the objects. This decision is binary when subjects prefer either the first object or the second object, but can also be ordinal when subjects make their decisions on a Likert scale. Since subject-specific covariates, which reflect characteristics of the subject, may affect the preference decision, it is essential to incorporate subject-specific covariates into the model. However, the inclusion of subject-specific covariates yields a model that contains many parameters and thus estimation becomes challenging. To overcome this problem, we propose a procedure that is able to select and estimate only relevant variables.

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“…Most of the available add-on packages for paired comparison models in R are restricted to the case of binary response and cannot deal with ordered response. The most popular packages are prefmod (Hatzinger and Dittrich, 2012) and BradleyTerry2 (Turner and Firth, 2012). The former uses the log linear representation of BT-models and can handle draws in the response variable.…”

Section: Discussionmentioning

confidence: 99%

Extended ordered paired comparison models with application to football data from German Bundesliga

Tutz

Schauberger

2014

AStA Adv Stat Anal

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A general paired comparison model for the evaluation of sports competitions is proposed. It efficiently uses the available information by allowing for ordered response categories and team-specific home advantage effects. Penalized estimation techniques are used to identify clusters of teams that share the same ability. The model is extended to include team-specific explanatory variables. It is shown that regularization techniques allow to identify the contribution of explanatory variables to the success of teams. The usefulness of the methods is demonstrated by investigating the performance and its dependence on the budget for football teams of the German Bundesliga.

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prefmod: AnRPackage for Modeling Preferences Based on Paired Comparisons, Rankings, or Ratings

Cited by 80 publications

References 19 publications

Exploring Curved Schematization of Territorial Outlines

Exploring Curved Schematization of Territorial Outlines

Subject-specific Bradley–Terry–Luce models with implicit variable selection

Extended ordered paired comparison models with application to football data from German Bundesliga

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