Dear list, I am new to PCMs and confused about model assumptions using Pgls. I am trying to determine the association between a discrete (categorial) predictor and a continuous response variable across species using a block of post burn-in trees. I have assessed model fit of the continuous response using fitContinuous() assessing 4 models of evolution using AIC; Brownian Motion provides the best fit and estimating lambda with phylosig() returns a value =0.92 using the MCC tree. However, when I estimate lambda with the gls model its 0.004. In fact a model with lambda fixed to 0 provides a much better fit than the BM model or one where lambda is fixed to that estimated using phylosig(). Somewhere in the process I've thoroughly confused myself about whether there is phylogenetic independence of residuals error for dependent variable in the gls model, and which is the appropriate model to use (i.e., ols vs Pgls)?
Also, when trying to estimate phylogenetic signal for each gls model across the block of trees my script crashes. From my understanding, this can happen when lambda is very close to 0 or 1, and when I fix lambda to a value the script runs fine. Considering the above, I was wondering if anyone has any recommendations about how/whether to optimize lambda? If needed, would be happy to send my script and data. Thanks for all your help, Jesse PhD student Boston University [[alternative HTML version deleted]] _______________________________________________ R-sig-phylo mailing list - R-sig-phylo@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-phylo Searchable archive at http://www.mail-archive.com/r-sig-phylo@r-project.org/