[R] modelling a particular design

Andreas Bartsch spielbergo at t-online.de
Sat May 11 22:28:12 CEST 2002

Dear Professor Ripley, dear list members-
thanks a lot for your comments. I am sorry for the missing basics - I wrote
the email after an on-call service this morning.
y is indeed continuous. x and z are as well, fx is ordinal (x trichotomized
at cutoffs). And yes, I think I am after a linear mixed effects model (by
GLM I meant a general linear modelling) but again: I am not sure and don´t
know how to treat the data. I will read through the nlme examples but maybe
someone may point me to crucial aspects my description was missing and a
very brief outline how to proceed using nlme.
TIA for any comments-

----- Original Message -----
From: <ripley at stats.ox.ac.uk>
To: "Andreas Bartsch" <bartsch at neuroradiologie.uni-wuerzburg.de>
Cc: <r-help at stat.math.ethz.ch>
Sent: Saturday, May 11, 2002 6:31 PM
Subject: Re: [R] modelling a particular design

A lot of basic information is missing.  For example, is y a continuous
measurement?  What do you mean by GLM? (Warning, SAS and R mean different
things.) ....

If y is indeed continuous, I think lme (in R's recommended package nlme)
will do what you want, with a random effect for subject.  It does need
some learning.  The Pinheiro & Bates book will help a lot, provided you
have the background knowledge they presume.

AFAIK there is no suitable software in Omegahat.

On Sat, 11 May 2002, Andreas Bartsch wrote:

> Dear R- and Omega-list-members,
> I am trying to make statistical inference about the following design:
> A dependent variable y has been measured multiple times, i.e. 4 times
> (y1,y2, y3, y4), unfortunately suffering from some successive dropouts
> the sample sizes varies for y1, y2, y3, and y4). For every y, two other
> variables (covariates) were also measured: x & z, and both do presumably
> exert an effect on y. At some cutoffs, x can also be trichotomized into 3
> ordinal levels constituting a presumed factor (fx) influencing the level
> y at all of the different measurings (1-4). x and fx are rather stable
> arcoss the 4 measurements whereas z is not. H0 is that x and fx are not
> influencing the level of y (i.e. explaining any variance of y)
> of  (i.e. controlled for) z at any of the measurements.
> (1) What would be a good way of testing for the hypothesis in the context
> a GLM or a canonical regression analysis? I can see that in a parametric
> testing and for a single measurement of y a simple multiple linear
> regression (MLR) or an ANCOVA (or a RANCOVA in a nonparametric approach)
> would do the trick. However, I am not sure how to tackle the issue facing
> repeated and at least biologically somehow interdependent measurements and
> the goal to include as much measurements as possible even though the
> size differs. I thought about running an MLR for x and an ANCOVA for fx
> seperately for y1, y2, y3 and y4 but I am not sure if this would require a
> correction for multiple testings and if this is in fact the best approach
> all. Pooling all 4 measurements, on the other hand, would mean to pretend
> that they were all derived from different subjects which is clearly not
> case. So, basically I don´t really know how to treat the testing.
> (2) Is there an implementation in R or Omega to perform such testing? How
> would that run?
> Hopefully, the question isn´t too trivial to the list - I am not a
> statisticician and just fed up with the comercial stats software... Thank
> you very much in advance-
> Andreas Bartsch, MD
> -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.
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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 272860 (secr)
Oxford OX1 3TG, UK                Fax:  +44 1865 272595

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