Thanks so much everyone for stopping by!

A new paper out in Ecology by Xiao and colleagues (in press, here) compares the use of log-transformation to non-linear regression for analyzing power-laws.

They suggest that the error distribution should determine which method performs better. When your errors are additive, homoscedastic, and normally distributed, they propose using non-linear regression. When errors are multiplicative, heteroscedastic, and lognormally distributed, they suggest using linear regression on log-transformed data. The assumptions about these two methods are different, so cannot be correct for a single dataset.

They will provide their R code for their methods once they are up on Ecological Archives (they weren't up there by the time of this post).

I am writing a set of functions to search ITIS for taxonomic information (more databases to come) and functions to fetch plant phylogenetic trees from Phylomatic. Code at github.

Also, see the examples in the demos folder on the Github site above.

I did a little simulation to examine how K and lambda vary in response to tree size (and how they compare to each other on the same simulated trees). I use Liam Revell's functions fastBM to generate traits, and phylosig to measure phylogenetic signal.

Two observations: 


First, it seems that lambda is more sensitive than K to tree size, but then lambda levels out at about 40 species, whereas K continues to vary around a mean of 1.

Second, K is more variable than lambda at all levels of tree size (compare standard error bars).

Does this make sense to those smart folks out there?

UPDATE: Greg jordan has a much more elegant way of plotting trees with ggplot2. See his links in the comments below.


I wrote a simple function for plotting a phylogeny in ggplot2. However, it only handles a 3 species tree right now, as I haven't figured out how to generalize the approach to N species.

Any ideas on how to improve this?