{"data":{"id":"10.6084/m9.figshare.c.2034698","type":"dois","attributes":{"doi":"10.6084/m9.figshare.c.2034698","prefix":"10.6084","suffix":"m9.figshare.c.2034698","identifiers":[],"alternateIdentifiers":[],"creators":[{"name":"Kang, Lulu","nameType":"Personal","givenName":"Lulu","familyName":"Kang","affiliation":[],"nameIdentifiers":[]},{"name":"Salgado, Javier Cruz","nameType":"Personal","givenName":"Javier Cruz","familyName":"Salgado","affiliation":[],"nameIdentifiers":[]},{"name":"Brenneman, William A.","nameType":"Personal","givenName":"William A.","familyName":"Brenneman","affiliation":[],"nameIdentifiers":[]}],"titles":[{"title":"Comparing the Slack-Variable Mixture Model With Other Alternatives"}],"publisher":"Figshare","container":{},"publicationYear":2015,"subjects":[{"subject":"Genetics"},{"subject":"FOS: Biological sciences","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"FOS: Biological sciences","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"Ecology"},{"subject":"69999 Biological Sciences not elsewhere classified","subjectScheme":"FOR"},{"subject":"80699 Information Systems not elsewhere classified","subjectScheme":"FOR"},{"subject":"FOS: Computer and information sciences","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"FOS: Computer and information sciences","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"19999 Mathematical Sciences not elsewhere classified","subjectScheme":"FOR"},{"subject":"FOS: Mathematics","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"FOS: Mathematics","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"Science Policy"}],"contributors":[],"dates":[{"date":"2015-12-05","dateType":"Created"},{"date":"2016-05-20","dateType":"Updated"},{"date":"2015","dateType":"Issued"}],"language":null,"types":{"ris":"GEN","bibtex":"misc","citeproc":"article","schemaOrg":"Collection","resourceType":"Collection","resourceTypeGeneral":"Collection"},"relatedIdentifiers":[{"relationType":"IsSupplementTo","relatedIdentifier":"10.1080/00401706.2014.985389","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":[],"formats":[],"version":null,"rightsList":[{"rights":"CC-BY","rightsUri":"http://creativecommons.org/licenses/by/3.0/us"}],"descriptions":[{"description":"There have been many linear regression models proposed to analyze mixture experiments including the Scheffé model, the slack-variable model, and the Kronecker model. The use of the slack-variable model is somewhat controversial within the mixture experiment research community. However, in situations that the slack-variable ingredient is used to fill in the formulation and the remaining ingredients have constraints such that they can be chosen independently of one another, the slack-variable model is extremely popular by practitioners mainly due to the ease of interpretation. In this article, we advocate that for some mixture experiments the slack-variable model has appealing properties including numerical stability and better prediction accuracy when model-term selection is performed. We also explain how the effects of the slack-variable model components should be interpreted and how easy it is for practitioners to understand the components effects. We also investigate how to choose the slack-variable component, what transformation should be used to reduce collinearity, and under what circumstances the slack-variable model should be preferred. Both simulation and practical examples are provided to support the conclusions.","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[],"xml":"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