From Spider-Man to Hero - Archetypal Analysis in<i>R</i>

Eugster, Manuel J. A.; Leisch, Friedrich

doi:10.18637/jss.v030.i08

Cited by 115 publications

(137 citation statements)

References 6 publications

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“…In addition, the clustering of k-means and related methods tends to place too much emphasis on PC1 and PC2 at the expense of lower PCs (based both on previous reports and observations from our own data; ref. 22). Note the importance of PC3 in this analysis in revealing the continuum in ABMR.…”

Section: Discussionmentioning

confidence: 93%

Assessing rejection-related disease in kidney transplant biopsies based on archetypal analysis of molecular phenotypes

et al. 2017

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Section: Discussionmentioning

confidence: 93%

Assessing rejection-related disease in kidney transplant biopsies based on archetypal analysis of molecular phenotypes

et al. 2017

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“…The speed of our algorithm depends on the efficiency of the convex least squares method, as was the case with the archetype algorithm implemented by Eugster and Leisch (2009). We use the penalized non-negative least squares method, that according to Cutler and Breiman (1994), is relatively slow but can be used if the number of variables is larger than the number of observations.…”

Section: Methodsmentioning

confidence: 99%

“…Archetypes and archetypoids are computed by means of the archetypes R package (Eugster and Leisch, 2009;R Development Core Team, 2016) and the Anthropometry R package Vinué, 2017), respectively.…”

Section: Sports Applicationsmentioning

confidence: 99%

Archetypoid analysis for sports analytics

Vinué

Epifanio

2017

Data Min Knowl Disc

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We intend to understand the growing amount of sports performance data by finding extreme data points, which makes human interpretation easier. In archetypoid analysis each datum is expressed as a mixture of actual observations (archetypoids). Therefore, it allows us to identify not only extreme athletes and teams, but also the composition of other athletes (or teams) according to the archetypoid athletes, and to establish a ranking. The utility of archetypoids in sports is illustrated with basketball and soccer data in three scenarios. Firstly, with multivariate data, where they are compared with other alternatives, showing their best results. Secondly, despite the fact that functional data are common in sports (time series or trajectories), functional data analysis has not been exploited until now, due to the sparseness of functions. In the second scenario, we extend archetypoid analysis for sparse functional data, furthermore showing the potential of functional data analysis in sports analytics. Finally, in the third scenario, features are not available, so we use proximities. We extend archetypoid analysis when asymmetric relations are present in data. This study provides information that will provide valuable knowledge about player/team/league performance so that we can analyze athlete's careers.

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“…Therefore, it should be started with different initial values several times to obtain a global optimum. The algorithm was executed in R using the package "archetypes" by Eugster and Leisch [30].…”

Section: Spatiotemporal Modes Of Seasonal Extreme Precipitationmentioning

confidence: 99%

Spatiotemporal Variability of Extreme Summer Precipitation over the Yangtze River Basin and the Associations with Climate Patterns

Hao

Yuan

et al. 2017

Water

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Abstract:Understanding the spatiotemporal variability of seasonal extreme precipitation and its linkage with climate patterns is of great importance for water resource management over the Yangtze River Basin. Hence, this study examined the spatiotemporal variability of seasonal extreme precipitation through the archetypal analysis (AA), by which observations were decomposed and characterized as several extreme modes. Six archetypes were identified and can obviously exhibit the features of events with above average or below average precipitation. Summer precipitation is the most variable compared to the winter, spring, and autumn precipitation through the trend analysis. It ranged from extremely dry (A6) to normal (A1 and A2) to extremely wet (A4). Climate teleconnections to the four archetypes for summer precipitation and relative importance of climate patterns were thus investigated. Results show that El Niño Southern Oscillation index is the strongest determinant of the ensuing archetypes representing the events with above average precipitation, while the Atlantic Multi-decadal Oscillation (AMO) contributes most to the events with below-average precipitation. A warm phase of the Pacific Decadal Oscillation (PDO) is significantly correlated with the above-average precipitation.

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From Spider-Man to Hero - Archetypal Analysis inR

Cited by 115 publications

References 6 publications

Assessing rejection-related disease in kidney transplant biopsies based on archetypal analysis of molecular phenotypes

Assessing rejection-related disease in kidney transplant biopsies based on archetypal analysis of molecular phenotypes

Archetypoid analysis for sports analytics

Spatiotemporal Variability of Extreme Summer Precipitation over the Yangtze River Basin and the Associations with Climate Patterns

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