Hi Everyone, Thanks for your responses. I guess I failed to explain what I really meant. Apologies.
I have a time-scaled ultrametric tree and a binary character (and no missing data) and I want to fit a true Dollo character (exactly as Brian describes with: 1) a root state of zero, 2) a single gain, and 3) losses if required). The thing I want to get at is those losses, and ideally using stochastic character mapping so I have both where and when and uncertainties in both. If I understand correctly the matrix(c(0,0,1,0),2,2) model won’t quite do this so maybe ace/make.simmap can’t cope with this? Thanks again, Graeme > On 15 Sep 2015, at 10:36, Liam J. Revell <liam.rev...@umb.edu> wrote: > > Hi Graeme. > > What Matt says is correct, of course, but it is also true that you can do > this with ace or make.simmap. > > That would look like: > > model<-matrix(c(0,0,1,0),2,2) > rownames(model)<-colnames(model)<-0:1 > ## ace > fit<-ace(x,tree,type="discrete",model=model) > ## make.simmap > trees<-make.simmap(tree,x,model=model,nsim=100) > > You (should) get the exact same parameter estimates for the rate 1->0 for all > three methods, so it depends what you want to do: stochastic mapping, > marginal ASR, joint ASR, or simply estimate the parameters of the Mk model. > > BUT (and I see that Brian just responded with something along the same lines) > a Dollo character does not imply that 0->1 does not occur, but that it occurs > only once. If the root state is 0, not 1, then this could make things a > little more complicated; however one possibility (and, to be fair, I haven't > given this too much thought), would be to put a strong prior on the root to > be 0 and the transition 0->1 to be low (but non-zero) using stochastic > mapping. > > All the best, Liam > > Liam J. Revell, Associate Professor of Biology > University of Massachusetts Boston > web: http://faculty.umb.edu/liam.revell/ > email: liam.rev...@umb.edu > blog: http://blog.phytools.org > > On 9/14/2015 8:15 PM, Matt Pennell wrote: >> ## make a likelihood function >> lik <- make.mk2(t, d) >> >> ## constrain the likelihood so that it only allows forward transitions >> lik_con <- constrain(lik, q10~0) >> >> ## estimate the MLE >> fit <- find.mle(lik_con, 0.1) >> >> ## use parameters to do ASR using ML parameters. Other operations also >> possible. >> asr <- asr.joint(lik_con, fit$par) _______________________________________________ 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/
