lmdme: Linear Models on Designed Multivariate Experiments inR

lmdme: Linear Model on Designed Multivariate Experiments in R

The lmdme package implements analysis of variance (ANOVA) decomposition through linear models on designed multivariate experiments in R (R Development Core Team, 2012), allowing ANOVA-principal component analysis (APCA) and ANOVA-simultaneous component analysis (ASCA). It also extends both methods with the application of partial least squares (PLS) through the specification of a desired output ...

Multivariate Linear Models

For example, we may have (i) measurements of d = 5 air pollutants (CO, NO, etc.) on n = 42 widely-separated days, (ii) d test scores for n different students, (iii) best results for d Olympic events for teams from n different countries, or (iv) d different physical measurements for n individuals (human or animal) that we are trying to classify. In each case, the i row corresponds to the i multi...

Multivariate Linear Path Models

Path analysis is useful to explain the interrelationships among sets of observed variables in a causal chain. However, path analysis has been underutilized in health science and epidemiology research, because of its restrictive requirement of a complete causal ordering of variables. In order to solve this problem, we suggest the use of multivariate path models and derive a multivariate ”Calculu...

Regression tree models for designed experiments

Although regression trees were originally designed for large datasets, they can profitably be used on small datasets as well, including those from replicated or unreplicated complete factorial experiments. We show that in the latter situations, regression tree models can provide simpler and more intuitive interpretations of interaction effects as differences between conditional main effects. We...

On Computer‐Designed Quantum Experiments