Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Templ, Matthias | - |
| dc.date.accessioned | 2018-05-28T13:18:33Z | - |
| dc.date.available | 2018-05-28T13:18:33Z | - |
| dc.date.issued | 2018 | - |
| dc.identifier.uri | https://www.youtube.com/watch?v=iJzeW0KYAaM | de_CH |
| dc.identifier.uri | https://2018.erum.io/#talk-2-44 | de_CH |
| dc.identifier.uri | https://digitalcollection.zhaw.ch/handle/11475/6167 | - |
| dc.description.abstract | Compositional data are nowadays widely accepted as multivariate observations carrying relative information. Compositional data follow the principle of scale invariance, typically being represented in proportions and percentages. In other words, for compositional data the relevant information is contained in the (log-)ratios between the components (parts). Compositional data are present in almost any field of research. Examples for compositional data are, for example, concentration of chemical elements in soil samples, time budget data, expenditures, tax or wage components or percentages and ratios reported in various tables. Through data from our favourite drinks, we will show the usefulness of the representation of data in isometric coordinates and the analysis of these coordinates instead of analysing the raw data on the simplex. As a side note of the talk, we want to answer such important questions of life: will the quality of beer mainly depend on age and how it should be stored? Should you drink blended coffee, or is Scottish Whisky really different to Irish or American Whiskey? We use the package “robCompositions” for all practical examples. | de_CH |
| dc.language.iso | en | de_CH |
| dc.rights | Licence according to publishing contract | de_CH |
| dc.subject | Compositional data analysis | de_CH |
| dc.subject.ddc | 510: Mathematik | de_CH |
| dc.title | Compositional data analysis of our favourite drinks | de_CH |
| dc.type | Konferenz: Sonstiges | de_CH |
| dcterms.type | Text | de_CH |
| zhaw.departement | School of Engineering | de_CH |
| zhaw.organisationalunit | Institut für Datenanalyse und Prozessdesign (IDP) | de_CH |
| zhaw.conference.details | eRum 2018, European R Users Meeting, Budapest, Bulgaria, 14-16 May 2018 | de_CH |
| zhaw.funding.eu | No | de_CH |
| zhaw.originated.zhaw | Yes | de_CH |
| zhaw.publication.status | publishedVersion | de_CH |
| zhaw.publication.review | Keine Begutachtung | de_CH |
| Appears in collections: | Publikationen School of Engineering | |
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Templ, M. (2018). Compositional data analysis of our favourite drinks. eRum 2018, European R Users Meeting, Budapest, Bulgaria, 14-16 May 2018. https://www.youtube.com/watch?v=iJzeW0KYAaM
Templ, M. (2018) ‘Compositional data analysis of our favourite drinks’, in eRum 2018, European R Users Meeting, Budapest, Bulgaria, 14-16 May 2018. Available at: https://www.youtube.com/watch?v=iJzeW0KYAaM.
M. Templ, “Compositional data analysis of our favourite drinks,” in eRum 2018, European R Users Meeting, Budapest, Bulgaria, 14-16 May 2018, 2018. [Online]. Available: https://www.youtube.com/watch?v=iJzeW0KYAaM
TEMPL, Matthias, 2018. Compositional data analysis of our favourite drinks. In: eRum 2018, European R Users Meeting, Budapest, Bulgaria, 14-16 May 2018 [online]. Conference presentation. 2018. Verfügbar unter: https://www.youtube.com/watch?v=iJzeW0KYAaM
Templ, Matthias. 2018. “Compositional Data Analysis of Our Favourite Drinks.” Conference presentation. In eRum 2018, European R Users Meeting, Budapest, Bulgaria, 14-16 May 2018. https://www.youtube.com/watch?v=iJzeW0KYAaM.
Templ, Matthias. “Compositional Data Analysis of Our Favourite Drinks.” eRum 2018, European R Users Meeting, Budapest, Bulgaria, 14-16 May 2018, 2018, https://www.youtube.com/watch?v=iJzeW0KYAaM.
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