[R] Logistic regression: categorical predictor and continuous response
Prof Brian Ripley
ripley at stats.ox.ac.uk
Wed Jun 23 08:26:40 CEST 2004
You can a logistic non-linear regresion with a continuous response -- see
SSlogis in package stats (using nls).
However, I think
> I believe I should run a logistic regression on this
is the problem. You have six groups of multivariate observations and you
want to know if and how they differ by group(?) If so the usual
- linear and quadratic discriminant analysis, very sensitive to joint
normality. (lda and qda in package MASS),
- logistic discrimination. As you have > 2 groups, use multinom in
If you were here I would be directing you to our statistics consulting
service -- do you have a local source of statistics help?
On Tue, 22 Jun 2004, Christine Parent wrote:
> Hi all,
> I am fairly new to R, and not a stats expert. I am having trouble finding
> information to help me analyzing my set of data.
> I got morphological data on land snail shells of different species from the
> Galapagos Islands. Each island has up to 6 different vegetation zones, and
> snails have adapted to up to 4 of these zones on each of the major islands.
> I would like to test if there is any correlation between the morphology of
> the shells which is continuous (I ran a PCA analysis on the morphological
> data, and the variation can be parted into two main principal components:
> PC1 for size and PC2 for shape) and the vegetation zone where these snails
> are found (which is a categorical predictor variable).
> I believe I should run a logistic regression on this, but all I could find
> in R was an analysis where the predictor was continuous and the response was
> categorical. Could anyone direct me to the relevant ref or how to write the
> proper script to run a logistic regression analysis where the predictor
> would be categorical and the response continuous?
> Any help or advice on this would be greatly appreciated!
> Many thanks,
> Christine Parent
> Ph.D. candidate
> Department of Biological Sciences
> Simon Fraser University
> cparent at sfu.ca
Brian D. Ripley, ripley at stats.ox.ac.uk
Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/
University of Oxford, Tel: +44 1865 272861 (self)
1 South Parks Road, +44 1865 272866 (PA)
Oxford OX1 3TG, UK Fax: +44 1865 272595
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