Dear Julien,
maybe I dont understand your rmodel...but IF your
model has one continuous dep. and one categorical
(binary) indep. it looks like an ANOVA model: in this
case phy.anova() [or phy.manova() if you have >1
dependents] in the R package geiger does it. IF the
model is different .......please explain better.
Inverting the dependence-independence
relationship....depends on your hypothesis testing.
When the categorical becomes the dependent you need to
apply a phylogentic logistic regression (in the case
of binary) or multinomial logistic (I think MCMCglmm
does it).
IF you have more factor variables as
dependent...applying comparative methods maybe is more
complicated but a trick could be useful.
Say you have a two-levels and a four levels factor
variables. You can test ***** pairwise *****(!!) your
(**CONTINUOUS**!!) dependent against a new factor
where you coded any possible level identifiable by all
occurring combinations of the levels of the two factor
variables. Not necessarily there will be a n°levels
equal to the n°levels of fisrt factor variable *
n°levels of the second one: it depends from real data.
But you wrote: "symbiont location ~ habitat VS habitat
~ symbiont location"
here....all things are categorical....is it?...Where
is the body size?
Best
Paolo
Dear colleagues,
I am testing the impact of categorical binary
characters (habitat and presence/absence of symbionts)
on a continuous variable (log of body size) using
PGLS...
I am not sure if I should remove the intercept from
the formulae and the biological interpretation of the
absence of intercept for categorical variables.. All
papers I found on the issue of regression through
origin were about PIC and continuous characters
It does not change my conclusions when I test
individually each variable (BOTH have a HIGHLY
significant impact on body size), but it does when I
test them simultaneously:
Coefficients:
Estimate Std. Error t value
Pr(>|t|)
(Intercept) 3.252335 0.731056 4.4488
5.115e-05 ***
Habitat1 0.706823 0.434013 1.6286
0.1099
Location1 0.598868 0.810679 0.7387
0.4637
Habitat1:Location1 -0.078772 0.905514 -0.0870
0.9310
F-statistic: 3.744 on 4 and 48 DF, p-value: 0.009906
Coefficients:
Estimate Std. Error t value
Pr(>|t|)
Habitat0 3.252335 0.731056 4.4488
5.115e-05 ***
Habitat1 3.959158 0.782851 5.0574
6.629e-06 ***
Location1 0.598868 0.810679 0.7387
0.4637
Habitat1:Location1 -0.078772 0.905514 -0.0870
0.9310
F-statistic: 3.744 on 4 and 48 DF, p-value: 0.009906
Also, the output of the above analysis depends on the
order of variables (symbiont location ~ habitat VS
habitat ~ symbiont location).. Once the effect of one
variable is removed, the effect of the other one is no
longer significant, likely because both are
correlated. Is there an objective way to decide which
one explains the best the body size ? Perhaps
considering amount of variance explained by each
variable individually ?
Apologies for these very naive questions.. I guess
answers are obvious to most of you but...
Thanks in advance for your comments.
Regards
Julien Lorion
PhD, Post-doctoral fellow of the Japan Society for the
Promotion of Science
Japan Agency for Marine-Earth Science and Technology
(JAMSTEC)
Marine Ecosystems Research Department
2-15 Natsushima, Yokosuka 237-0061 Japan
Phone: +81-46-867-9570, Fax: +81-46-867-9525
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--
Paolo Piras
Center for Evolutionary Ecology
and
Dipartimento di Scienze Geologiche, Università Roma Tre
Largo San Leonardo Murialdo, 1, 00146 Roma
Tel: +390657338000
email: ppi...@uniroma3.it
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