On 12/05/2008, at 4:52 AM, Federico Calboli wrote:
On 10 May 2008, at 07:36, Kingsford Jones wrote:
Federico,
I think you'll be more likely to receive the type of response you're
looking for if you formulate your question more clearly. The
inclusion of commented, minimal, self-contained, reproducible code
(as is requested at the bottom of every email sent by r-help) is an
effective way to clarify the issues. Also, when asking a question
about fitting a model it's helpful to describe the specific research
questions you want the model to answer.
snip
I apprecciate that my description of the *full* model is not 100%
clear, but my main beef was another.
The main point of my question is, having a 3 way anova (or ancova,
if you prefer), with *no* nesting, 2 fixed effects and 1 random
effect, why is it so boneheaded difficult to specify a bog standard
fully crossed model? I'm not talking about some rarified esoteric
model here, we're talking about stuff tought in a first year Biology
Stats course here[1].
Now, to avoid any chances of being misunderstood in my use of the
words 'fully crossed model', what I mean is a simple
y ~ effect1 * effect2 * effect3
with effect3 being random (all all the jazz that comes from this
fact). I fully apprecciate that the only reasonable F-tests would be
for effect1, effect2 and effect1:effect2, but there is no way I can
use lme to specify such simple thing without getting the *wrong*
denDF. I need light on this topic and I'd say it's a general enough
question not to need much more handholding than this.
There is only one random effect, so where does the crossing come
from ? The fixed effects vary across blocks, but they are fixed so are
just covariates. For this type of data the usual model in lme4 is
y~fixed1+fixed2+1|group and for lme split into fixed and random parts.
Having said that, I did look at the mixed-effects mailing list
before posting here, and it looks like it was *not* the right place
to post anyway:
'This mailing list is primarily for useRs and programmeRs interested
in *development* and beta-testing of the lme4 package.'
although the R-Me is now CC'd in this.
I fully apprecciate that R is developed for love, not money, and if
I knew how to write an user friendly frontend for nlme and lme4 (and
I knew how to actually get the model I want) I'd be pretty happy to
do so and submit it as a library. In any case, I feel my complaint
is pefectly valid, because specifying such basic model should
ideally not such a chore, and I think the powers that be might
actually find some use from user feedback.
The problems seems to be that you want lme to work in the same way as
an ANOVA table and it doesn't. The secret with lme and lme4 is to
think about the structure of the data and describe with an equation.
Then each term in the equation corresponds to part of the model
definition in R.
Once I have sorted how to specify such trivial model I'll face the
horror of the nesting, in any case I attach a toy dataset I created
especially to test how to specify the correct model (silly me).
I'm a bit lost with your data file, it has 4 covariates, which is more
than enough for 2 fixed effects, assuming block is the grouping and y
the outcome.
Ken
Best,
Federico Calboli
[1] So much bog standard that the Zar, IV ed, gives a nice table of
how to compute the F-tests correctly, taking into account that one
of the 3 effects is randon (I'll send the exact page and table
number tomorrow, I don't have the book at home).
testdat.txt
--
Federico C. F. Calboli
Department of Epidemiology and Public Health
Imperial College, St. Mary's Campus
Norfolk Place, London W2 1PG
Tel +44 (0)20 75941602 Fax +44 (0)20 75943193
f.calboli [.a.t] imperial.ac.uk
f.calboli [.a.t] gmail.com
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