Contents
How to do a multivariate analysis in R?
This booklet tells you how to use the R statistical software to carry out some simple multivariate analyses, with a focus on principal components analysis (PCA) and linear discriminant analysis (LDA).
Which is the best book for multivariate analysis?
To learn about multivariate analysis, I would highly recommend the book “Multivariate analysis” (product code M249/03) by the Open University, available from the Open University Shop. There is a book available in the “Use R!” series on using R for multivariate analyses, An Introduction to Applied Multivariate Analysis with R by Everitt and Hothorn.
When to use symmetric methods to compare multivariate data?
Sections 10.5 (p. 597) and 11.5 (p. 696) from: Legendre, P. and L. Legendre. 2012. Numerical Ecology. Elsevier. link Skim the math to whatever degree you desire. Use a symmetric methods when you don’t have a hypothesis about the direction of effects between two matrices. Examples? Counter-examples?
Can a multivariate comparison of variance be meaningful?
Comparisons of pattern and magnitude of phenotypic variation are central to many studies in evolution and ecology, but a meaningful comparison of multivariate variance patterns can be challenging.
How to calculate relative importance of predictors in R?
R has a package for calculating relative importance. Ulrike Grömping, who maintains the CRAN Task View for Design of Experiments, has written an R package called relaimpo. More importantly, she has a website with references to everything you need to know about relative importance.
When to use relative importance in multiple regression?
This is an essential point to understand when we look at multiple regression with observational data, where the variables are not independent and not directly manipulated. In a conjoint study relative importance is defined as percentage contribution.
Is it easy to run multiple regression in R?
The multiple regression is easy to run using the linear model function in R. First, we standardize the rating scores so that the regression coefficients will be standardized weights. What is the one thing that we ought to do?