Mixture Experiments inRUsingmixexp

Efficient designs for constrained mixture experiments

Many practical experiments on mixtures (where the components sum to one) include additional lower or upper bounds on components, or on linear combinations of them. Usually theory cannot be used to obtain a good design, and algorithmic methods are necessary. Some of the available methods are discussed. Their performance is evaluated on some examples, and the form of the optimal design is investi...

Invariant Symmetric Block Matrices for the Design of Mixture Experiments

Bayesian analysis of factorial experiments by mixture modelling

A Bayesian analysis for factorial experiments is presented, using finite mixture distributions to model the main effects and interactions. This allows both estimation and an analogue of hypothesis testing in a posterior analysis using a single prior specification. A detailed formulation based on this approach is provided for the case of the two-way model with replication, allowing interactions....

Mixture modelling of gene expression data from microarray experiments

MOTIVATION Hierarchical clustering is one of the major analytical tools for gene expression data from microarray experiments. A major problem in the interpretation of the output from these procedures is assessing the reliability of the clustering results. We address this issue by developing a mixture model-based approach for the analysis of microarray data. Within this framework, we present nov...

Mixture Experiments and Their applications in Agricultural Research

Statistical techniques used to analyze data from Mixture Experiments involve fitting Multiple Regression models with the intercept set to zero, Canonical Analysis to determine the shape of the fitted response, and the Response Surface System. One can use Mixture Design Macros (e.g., ADXMIX File) in SAS or the point-and-click ADX Interface of SAS to analyze data and obtain graphs. Contour plot, ...