Hi all,
I'm still unsure of how I should interpret results given that using PGLS
to predict group size from brain size gives different significance
levels and lambda estimates than when I do the reverse (i.e., predict
brain size from group size). Biologically, I don't think this makes any
sense. If lambda is an estimate of the phylogenetic signal, what
possible evolutionary and biological sense are we to make if the
estimates of lambda are significantly different depending on which way
the association is assessed? I understand the mathematics may allow
this, but if I can't make sense of this biologically, then doesn't it
call into question the use of this method for these kinds of questions
in the first place? What am I missing here?
Here is some results from data I have that illustrate this (notice that
the lambda values are significantly different from each other):
Group size predicted by brain size:
model.group.by.brain<-pgls(log(GroupSize) ~ log(AvgBrainWt), data =
primate_tom, lambda='ML')
summary(model.group.by.brain)
Call:
pgls(formula = log(GroupSize) ~ log(AvgBrainWt), data = primate_tom,
lambda = "ML")
Residuals:
Min 1Q Median 3Q Max
-0.27196 -0.07638 0.00399 0.10107 0.43852
Branch length transformations:
kappa [Fix] : 1.000
lambda [ ML] : 0.759
lower bound : 0.000, p = 4.6524e-08
upper bound : 1.000, p = 2.5566e-10
95.0% CI : (0.485, 0.904)
delta [Fix] : 1.000
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.080099 0.610151 -0.1313 0.895825
log(AvgBrainWt) 0.483366 0.136694 3.5361 0.000622 ***
---
Signif. codes: 0 �***� 0.001 �**� 0.01 �*� 0.05 �.� 0.1 � � 1
Residual standard error: 0.1433 on 98 degrees of freedom
Multiple R-squared: 0.1132, Adjusted R-squared: 0.1041
F-statistic: 12.5 on 2 and 98 DF, p-value: 1.457e-05
Brain size predicted by group size:
model.brain.by.group<-pgls(log(AvgBrainWt) ~ log(GroupSize), data =
primate_tom, lambda='ML')
summary(model.brain.by.group)
Call:
pgls(formula = log(AvgBrainWt) ~ log(GroupSize), data = primate_tom,
lambda = "ML")
Residuals:
Min 1Q Median 3Q Max
-0.38359 -0.08216 0.00902 0.05609 0.27443
Branch length transformations:
kappa [Fix] : 1.000
lambda [ ML] : 1.000
lower bound : 0.000, p = < 2.22e-16
upper bound : 1.000, p = 1
95.0% CI : (0.992, NA)
delta [Fix] : 1.000
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.740932 0.446943 6.1326 1.824e-08 ***
log(GroupSize) 0.050780 0.043363 1.1710 0.2444
---
Signif. codes: 0 �***� 0.001 �**� 0.01 �*� 0.05 �.� 0.1 � � 1
Residual standard error: 0.122 on 98 degrees of freedom
Multiple R-squared: 0.0138, Adjusted R-squared: 0.003737
F-statistic: 1.371 on 2 and 98 DF, p-value: 0.2586
On Jul 14, 2013, at 6:18 AM, Emmanuel Paradis <emmanuel.para...@ird.fr>
wrote:
Hi all,
I would like to react a bit on this issue.
Probably one problem is that the distinction "correlation vs. regression" is
not the same for independent data and for phylogenetic data.
Consider the case of independent observations first. Suppose we are interested
in the relationship y = b x + a, where x is an environmental variable, say
latitude. We can get estimates of b and a by moving to 10 well-chosen
locations, sampling 10 observations of y (they are independent) and analyse
the 100 data points with OLS.
Here we cannot say anything about the correlation between x and y
because we controlled the distribution of x. In practice, even if x is
not controlled, this approach is still valid as long as the observations
are independent.
With phylogenetic data, x is not controlled if it is measured "on the species" -- in
other words it's an evolving trait (or intrinsic variable). x may be controlled if it is measured
"outside the species" (extrinsic variable) such as latitude. So the case of using
regression or correlation is not the same than above.
Combining intrinsic and extinsic variables has generated a lot of debate
in the literature.
I don't think it's a problem of using a method and not another, but rather to
use a method keeping in mind what it does (and its assumptions). Apparently,
Hansen and Bartoszek consider a range of models including regression models
where, by contrast to GLS, the evolution of the predictors is modelled
explicitly.
If we want to progress in our knowledge on how evolution works, I think we have
to not limit ourselves to assess whether there is a relationship, but to test
more complex models. The case presented by Tom is particularly relevant here
(at least to me): testing whether group size affects brain size or the
opposite (or both) is an
important question. There's been also a lot of debate whether
comparative data can answer this question. Maybe what we need here is an
approach based on simultaneous equations (aka structural equation
models), but I'm not aware whether this exists in a phylogenetic
framework. The approach by Hansen and Bartoszek could be a step in this
direction.
Best,
Emmanuel
Le 13/07/2013 02:59, Joe Felsenstein a �crit :
Tom Schoenemann asked me:
With respect to your crankiness, is this the paper by Hansen that you are
referring to?:
Bartoszek, K., Pienaar, J., Mostad, P., Andersson, S., & Hansen, T. F. (2012).
A phylogenetic comparative method for studying multivariate adaptation. Journal of
Theoretical Biology, 314(0), 204-215.
I wrote Bartoszek to see if I could get his R code to try the method mentioned
in there. If I can figure out how to apply it to my data, that will be great. I
agree that it is clearly a mistake to assume one variable is responding
evolutionarily only to the current value of the other (predictor variables).
I'm glad to hear that *somebody* here thinks it is a mistake (because it really is). I
keep mentioning it here, and Hansen has published extensively on it, but everyone keeps
saying "Well, my friend used it, and he got tenure, so it must be OK".
The paper I saw was this one:
Hansen, Thomas F & Bartoszek, Krzysztof (2012). Interpreting the evolutionary
regression: The interplay between observational and biological errors in
phylogenetic comparative studies. Systematic Biology 61 (3): 413-425. ISSN
1063-5157.
J.F.
----
Joe Felsenstein j...@gs.washington.edu
Department of Genome Sciences and Department of Biology,
University of Washington, Box 355065, Seattle, WA 98195-5065 USA
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_________________________________________________
P. Thomas Schoenemann
Associate Professor
Department of Anthropology
Cognitive Science Program
Indiana University
Bloomington, IN 47405
Phone: 812-855-8800
E-mail: t...@indiana.edu
Open Research Scan Archive (ORSA) Co-Director
Consulting Scholar
Museum of Archaeology and Anthropology
University of Pennsylvania
http://www.indiana.edu/~brainevo
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