Hi All-
I have spent an inordinate and embarrassing amount of time tracking down an
excruciatingly cryptic issue with read.dna, which I rarely use. Here are two
key problems:
1) The function automatically assumes it is reading DNA sequences when it
encounters a string of 10 continuous "DNA-li
Hi All-
I have spent an inordinate and embarrassing amount of time tracking down an
excruciatingly cryptic issue with read.dna, which I rarely use. Here are two
key problems:
1) The function automatically assumes it is reading DNA sequences when it
encounters a string of 10 continuous "DNA-li
Vanderlei-
Negative alpha would imply a detractor, rather than an attractor,
similar to what was found by Alroy (1996) in his Cope's Rule paper. Is
that the sort of dynamic you wanted to simulate?
I'm not familiar with any discussion of negative alpha values in the literature.
-Dave
On Wed, Apr 2
Hi Nina and everyone,
One thing to consider is that not all zero data are the same. Zeros under a
model of continuous trait evolution with a gaussian process as assumed under
Brownian motion and OU processes would occasionally cross zero, maybe go
negative, etc. For example if you were modeling
Dear,
Could I simulate continuous character evolution using a Ornstein-Uhlenbeck
model (Function rTraitCont in the package ape) with a negative alpha value?
My intention is to simulate trait evolution with different phylogenetic signal
(less or more similar than expected under Brownian motion e
Read over the Blomberg et al. (2003) paper.
K is intended for continuous-valued traits and/or those evolving similar to
Brownian motion.
You could report it if you wished, but I would add that caveat if you do.
The randomization test should be robust in any case.
Cheers,
Ted
From: Nina Hobbhah
Thanks all for your helpful contributions! I will use phylosignal.
Ted, I'm not sure I understand your last comment, "when the data are not though
of as continuous-valued and/or evolving similar to Brownian motion". What do
you mean by that? Also, are you suggesting that I report the presence/ab
However, calculating a K statistic is strange when the data are not thought of
as continuous-valued and/or evolving similar to Brownian motion. The
randomization test is OK, however.
Cheers,
Ted
From: Alejandro Gonzalez [alejandro.gonza...@ebd.csic.es]
Sent: Wednesday, April 25, 2012 8:46 AM
Hello,
The library picante in R implements Blomberg et al (2003) K estimate, Liam's
phytools package does as well. Phytools has the added advantage, if I remember
correctly, of allowing users to estimate K including within species variation.
Cheers
Alejandro
On 25, Apr 2012, at 5:29 PM, Theo
Complementing Ted's comment, the randomization test in Blomberg et al.
(2003) and the K statistics is available the package "picante". Now,
caution is warranted because having many species with the same
phenotypic value in the dataset (i.e., many zeros) can dramatically
decrease the statistical
The Blomberg et al (2003) randomization test is implemented in the picante
package, with the function phylosignal(). It may be implemented elsewhere
as well.
Best,
Gene
--
Gene Hunt
Curator, Department of Paleobiology
National Museum of Natural History
Smithsonian Institution [NHB, MRC 121]
P.O
I would suggest the randomization test in Blomberg et al. (2003). This will
give a valid significance test of the null hypothesis of no phylogenetic
signal. By itself, it does not give a measure of the strength (or amount) of
phylogenetic signal. Not sure if it is implented in r. If not, I c
Dear fellow list users,
I would like to assess the magnitude of phylogenetic signal in two sets of
continuous data. Set 1 contains numerous zeros and is therefore non-normal. Set
2 contains very little variation and is non-normal due to underdispersion.
Given that both data sets are largely imm
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