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Summarizing and extending from the comments: "A Bayesian MAP estimate may coincide with an MLE. However, the posterior distribution has no equivalent from a likelihood perspective". What do you mean by "A Bayesian estimate"? Often, with Bayes, we will just summarize the data by the posterior distribution (assuming it exists, in this case, sometimes, with a ...


4

The most basic way to do this is to run a new, larger regression with all of your data. (That is, the data that had been used before, plus the data for the two new species.) You should create a dummy variable to indicate each species (obviously with one species as the reference level--I've explained dummy coding here). You will also want to add product ...


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There are a few published examples of a similar problem where the aim has been, e.g, to infer a tree from the HIV transmission events. Furthermore, there are a few experimentally generated phylogenies, where the ancestral sequences might have been sampled, also. There's a list of these publication with the data at http://phylonetworks.blogspot.fi/p/datasets....


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Look into the 'multiple regression on distance matrices' approach used in this paper. The method is implemented in the R package ecodist (function MRM). Lichstein, J.W. (2007). Multiple regression on distance matrices: a multivariate spatial analysis tool. Plant Ecol., 188, 117–131. Goslee, S.C. & Urban, D.L. (2007). The ecodist package for ...


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In addition to what JTT wrote. Your standard phylogenetic tree is unrooted and, more importantly, usually does not make the molecular clock assumption. That is fine, because most likely your old sequences are not literally the ancestral sequences, they are just very close to the ancestral sequences in question. In other words, you are just fine with ...


1

You always have the choice to assign arbitrary branch lengths to the tree. Such as, each length equals 1 (or any arbitrary constant) or branch lengths are proportional to the number of tips. You can use compute.brlen function from ape.


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it is a general issue about dummy variables. One of the levels is the reference level, that is, when Epitheliochorial and Hemochorial are 0, you get the values for the third level. My advice to you is that you read the basics of linear regression. Hope this helps Cheers


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I know this question was posted 2 months ago but it might still help the OP or someone else. First, I am not sure to understand which question you are trying to answer with your second model. But if I understood correctly, you have the mean and standard error estimates of the jumping distances and are trying to get the effect of the other parameters: arena,...


1

If your goal is to obtain p-values for the individual predictors, then using anova on your fitted pgls models does that. AIC will give you measures of the overall goodness of fit of each model, but doesn't tell you about the significance of individual parameters. In fact, you can have a model favored by AIC that includes parameters with non-significant p-...


1

The MCMCglmm package is well suited to incorporating repeated measures of interspecific data. The following code demonstrates how to account for repeated measures by specifying a random effect for species in the MCMCglmm function. The pedigree argument did not work for me, so I use ginverse to specify the covariance structure of the random effect (i.e., the ...


1

I appreciate the school example, but for simplicity I stay with the original example, which was: lmer(Dep~X1+X2+X3+(1|R2/R1)) (R2=Genus, R1=Species) You make two comments I can use the average values of traits for each R1 and then drop the R1 random effect, but then I lose lots of data Response variable has no variation within species So, within ...


1

I think I understand what you are trying to achieve (correct me if I'm wrong). You want to test the hypothesis that your trait has ordered states (I think that is what you mean by serial homologues). That is something you can do within BayesTraits, but I think you should be using multistate, not discrete. For multistate, your data would be coded as 0, 1,...,...


1

Once again, I ended up finding my own answer. The package is called "phytools", and the command is "fancyTree". You can visualize a projection of the phylogeny into trait space in 3 dimensions. tree <- pbtree(n=10,scale=10) Y <- sim.corrs(tree,vcv=matrix(c(1,0.75,0.75,1),2,2)) fancyTree(tree,type="traitgram3d",X=Y,control=list(spin=FALSE))


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Having a quick look at the wikipedia page I'm not sure what the fit is here with MCMC/Metropolis-Hastings algorithm. The models described there look like continuous time Markov chains. If that's right then you can get analytic results, and wouldn't need to resort to MCMC. Essentially the analytic solution is to do the eigendecomposition of Q into $VLV^T$. ...


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The first step I would recommend is introduce a dummy variable for each of the ordinal class (see comments at https://www.google.com/url?sa=t&source=web&rct=j&ei=B9r5U67pH8vfsASwq4GADQ&url=http://www.uta.edu/faculty/kunovich/Soci5304_Handouts/Topic%25208_Dummy%2520Variables.doc&cd=2&ved=0CCAQFjAB&usg=AFQjCNEX-TD7RjSYZ-...


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