-------- Original Message -------- Subject: Re: Principal coordinates analyses Date: Fri, 24 Jul 2009 11:08:55 +0200 From: [email protected] To: morphmet <[email protected]> References: <[email protected]> Dear Jessica! You should start by studying the classical paper by John C. Gower in the English statistical journal Biometrika, 1966. In this paper he formalizes the concept of Q-mode analysis of a data-matrix with the end in view of producing a graphical representation of points based on the statistical distances between them. Please note that the components of the latent vectors are not possible to reify, although some try this out of ignorance of what is implied by the geometry. The project was given to Gower by the renowned English biomathematician Maurice Bartlett in order to tidy up what was known as Q-mode factor analysis. Also to be noted is that the residual variability - ie variability remaining after the extraction of 2 latent roots should not be too high (too high is often considered to be > 50%). Practical details, and references to literature, can be obtained for example in Richard Reyment and Enrico Savazzi (1999). Aspects of Multivariate statistical analysis in geology. Elsevier. Another reference in which interrelationships between multivariate techniques are explored is the book by Reyment and Karl G. Joereskog (1993) entitled Applied Factor Analysis in the Natural Sciences. Karl was, until his retirement, Professor of mathematical statistics at the University of Uppsala. Richard A. Reyment Citerar morphmet <[email protected]>:
-------- Original Message -------- Subject: Principal coordinates analyses Date: Wed, 22 Jul 2009 11:30:21 -0700 (PDT) From: Camp, Jessica A <[email protected]> To: [email protected] <[email protected]> Hi all, I'm working with a data set that has a lot of missing information. I want to do PCAs to assess variability and disparity between my species, but making subsets to run them decreases my sample size until it's ridiculously small with both the number of specimens and variables. I was advised that doing PCOs instead might be a way to include more data while ignoring the gaps to some degree. I haven't had much luck finding literature that describes the pros and cons of PCOs and exactly what they do, though. Can anyone guide me to something that would? Or to another test I could try? Thanks, Jessica Camp -- Replies will be sent to the list. For more information visit http://www.morphometrics.org
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