Hi Danielle.

`With regard to the first problem, that is not an error. Likelihoods`

`obtained from continuous probability densities are not probabilities`

`(these can only be obtained by integrating the density function on a`

`finite interval) and thus can take values >1 (and thus log-likelihoods >0).`

`In my experience, the most common reason for the second problem is a`

`tree with zero-length terminal edges (and thus covariances between`

`species that are equal to their respective variances). The solution to`

`this *should not* be to add a very small value to the offending terminal`

`edges, as this can give very high weight to the associated tips. It`

`might be by pruning one tip or the other from the analysis, or by using`

`some reasonable criterion to modify the terminal edge lengths.`

All the best, Liam J. Revell Associate Professor, University of Massachusetts Boston Profesor Asistente, Universidad Católica de la Ssma Concepción web: http://faculty.umb.edu/liam.revell/, http://www.phytools.org On 8/12/2018 11:32 AM, Danielle Miller wrote:

Hi, I’m interested in using the OUCH package to estimate BM and OU parameters for a specific trait among many different trees. My goal is to determined which model is the most suitable for each tree, applying likelihood ratio test. As I’m a new user in R when it comes to phylogenetic analysis, I started by running the documentation example (Hansen, documentation page 11) and was surprised to see that the loglikelihood was a positive number BM: $call brown(data = otd[c("tarsusL", "beakD")], tree = ot) $sigma.squared [,1] [,2] [1,] 0.02878091 0.08897504 [2,] 0.08897504 0.43711838 $theta $theta$tarsusL [1] 3.020419 $theta$beakD [1] 1.826695 $loglik [1] 9.90115 As this number is crucial for further analysis - Is this a transformation of the resulting log likelihood? (e.g. -2 * log(L) as described in the paper) or am I missing something here..? In addition I have another issue, I have a tree constructed of ~400 viral genomes and their corresponding trait values. When I’m running the documentation script with my own data (in the same format) I get the following error: Error in solve.default(v, e) : system is computationally singular: reciprocal condition number = 1.59061e-17 I guess it says that my variance covariance matrix is not inversable, hence I manually tried to adjust the retol parameter in the Hansen function in order to make it work (however I’ll need to second guess my results?), but I still get the same error. Code example: h1 <- hansen( + tree=ot, + data=otd[c("k5")], + regimes=otd["regimes"], + fit=TRUE, + sqrt.alpha=1, + sigma=1, + maxit=500000, + reltol=1e-20, + method="Nelder-Mead" + ) I’ll be thankful for any advice or answer, Danielle_______________________________________________ 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/

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