Testing effects of experimental design factors using multi-way analysis
| Autor: | Ellen Mosleth Færgestad, Kristian Hovde Liland |
|---|---|
| Rok vydání: | 2009 |
| Předmět: |
Computer science
Process Chemistry and Technology Multiplicative function Variation (game tree) computer.software_genre Unobservable Computer Science Applications Analytical Chemistry Data set Statistics Analysis of variance Data mining Multi way analysis Model building Relevant information computer Spectroscopy Software |
| Zdroj: | Chemometrics and Intelligent Laboratory Systems. 96:172-181 |
| ISSN: | 0169-7439 |
| DOI: | 10.1016/j.chemolab.2009.01.007 |
| Popis: | Analysing experimental design data is usually performed by analysis of variance (ANOVA). In situations where the higher orders of interactions hold the most relevant information, generalised multiplicative analysis of variance (GEMANOVA), which is based on parallel factor analysis (PARAFAC), may be a useful supplement to ANOVA. By GEMANOVA the information in the data is compressed down to a few multiplicative components describing the main variation in the data including relevant interaction phenomena. GEMANOVA is best used as an explorative tool. Still there is a need for validation criteria to assist the model building. In the present publication we present such a validation criterion for GEMANOVA models based on bootstrap methodology. The method is demonstrated on a data set consisting of a pot experiment, measuring the nitrogen-to-sulfur ratio in wheat grown under different fertilising schemes. It was found that GEMANOVA revealed complex patterns in the data which were unobservable by ANOVA. |
| Databáze: | OpenAIRE |
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