Hi Emma,
Continuous predictors are no problem at all. You can mix both continuous
and categorial predictors if needed. I suppose your response are counts
(the number of bats that passes)? In that case a generalised linear
mixed model is more appropriate. With the lme4 package you could try
something like this:
library(lme4)
Model <- glmer(BatPasses ~ Width + Height + (1|Site), family = poisson)
HTH,
Thierry
PS There is a mailing list dedicated to mixed models: R-Sig-MixedModels
------------------------------------------------------------------------
----
ir. Thierry Onkelinx
Instituut voor natuur- en bosonderzoek / Research Institute for Nature
and Forest
Cel biometrie, methodologie en kwaliteitszorg / Section biometrics,
methodology and quality assurance
Gaverstraat 4
9500 Geraardsbergen
Belgium
tel. + 32 54/436 185
Thierry.Onkelinx at inbo.be
www.inbo.be
To call in the statistician after the experiment is done may be no more
than asking him to perform a post-mortem examination: he may be able to
say what the experiment died of.
~ Sir Ronald Aylmer Fisher
The plural of anecdote is not data.
~ Roger Brinner
The combination of some data and an aching desire for an answer does not
ensure that a reasonable answer can be extracted from a given body of
data.
~ John Tukey
-----Oorspronkelijk bericht-----
Van: r-help-bounces at r-project.org [mailto:r-help-bounces at r-project.org]
Namens Emma Stone
Verzonden: woensdag 11 maart 2009 15:29
Aan: r-help at r-project.org
Onderwerp: Re: [R] Mixed models fixed effects
Dear All,
This may sound like a dumb question but I am trying to use a mixed model
to
determine the predictors of bat activity along hedges within 8 sites. So
my
response is continuous (bat passes) my predictors fixed effects are
continuous (height metres), width (metres) etc and the random effect is
site - can you tell me if the fixed effects can be continuous as all
the
examples I have read show them as categorical, but this is not covered
in
any documents I can find.
Help!
Emma