Using factor analysis or principal components analysis or measurement-error models for biological measurements in archaeology?

December 31, 2011

(This article was first published on Statistical Modeling, Causal Inference, and Social Science » R, and kindly contributed to R-bloggers)

Greg Campbell writes:

I am a Canadian archaeologist (BSc in Chemistry) researching the past human use of European Atlantic shellfish. After two decades of practice I am finally getting a MA in archaeology at Reading. I am seeing if the habitat or size of harvested mussels (Mytilus edulis) can be reconstructed from measurements of the umbo (the pointy end, and the only bit that survives well in archaeological deposits) using log-transformed measurements (or allometry; relationships between dimensions are more likely exponential than linear).
Of course multivariate regressions in most statistics packages (Minitab, SPSS, SAS) assume you are trying to predict one variable from all the others (a Model I regression), and use ordinary least squares to fit the regression line. For organismal dimensions this makes little sense, since all the dimensions are (at least in theory) free to change their mutual proportions during growth. So there is no predictor and predicted, mutual variation of all the dimensions is the response (a Model II regression), and the fitted regression line must give equal weight to all the dimensions: common methods are major-axis (perpendicular distances between the line and all the points are minimised, in a principal-component-analysis way) and reduced major axis or standard-major-axis (perpendicular distances between the standardised points and the line are fitted, and then unstandardised).

I see that you literally wrote the book on regression. Do you know if it is possible to carry out major-axis or reduced-major-axis fitting in multiple linear regressions in SPSS, SAS or Systat (I know that it can’t be done in Minitab)?

Do you know if there are applications in R that carry out this type of analysis?

My reply: I’m a sucker for any email that begins, “I am a Canadian archaeologist.” I think there are various models out there that could work here, including factor analysis and measurement-error models. I’m no expert on this particular set of models, but they get used in psychometrics when there are many variable measurements. Maybe some commenters could help?

The post Using factor analysis or principal components analysis or measurement-error models for biological measurements in archaeology? appeared first on Statistical Modeling, Causal Inference, and Social Science.

To leave a comment for the author, please follow the link and comment on their blog: Statistical Modeling, Causal Inference, and Social Science » R. offers daily e-mail updates about R news and tutorials on topics such as: Data science, Big Data, R jobs, visualization (ggplot2, Boxplots, maps, animation), programming (RStudio, Sweave, LaTeX, SQL, Eclipse, git, hadoop, Web Scraping) statistics (regression, PCA, time series, trading) and more...

If you got this far, why not subscribe for updates from the site? Choose your flavor: e-mail, twitter, RSS, or facebook...

Comments are closed.


Mango solutions

plotly webpage

dominolab webpage

Zero Inflated Models and Generalized Linear Mixed Models with R

Quantide: statistical consulting and training




CRC R books series

Six Sigma Online Training

Contact us if you wish to help support R-bloggers, and place your banner here.

Never miss an update!
Subscribe to R-bloggers to receive
e-mails with the latest R posts.
(You will not see this message again.)

Click here to close (This popup will not appear again)