[R-sig-phylo] PGLS confidence intervals?
Hello all, A recent article in PNAS claims to have used PGLS to calculate "95% confidence intervals" on the slope of a PGLS for some primate data. However, I understand from this thread: http://www.mail-archive.com/r-sig-phylo@r-project.org/msg02631.html that it does not make mathematical sense to calculate confidence intervals on PGLS regressions. Am I misunderstanding something here, or is it in fact not legitimate to calculate confidence intervals from PGLS regressions (and therefore this paper is in error)? For what it is worth, the paper is: Barton, R. A., & Venditti, C. (2013). Human frontal lobes are not relatively large. Proceedings of the National Academy of Sciences, 110(22), 9001-9006. Furthermore, the confidence intervals in the paper appear to only reflect confidence in the _intercepts_, not in the actual slope itself. I say this because the confidence intervals on their figures all appear to be exactly parallel to the PGLS estimate, whereas I understood the confidence intervals for predictions of individual cases - at least for simple linear regression - to be narrower near the mean, and increasingly divergent as one looks farther from this point (because the confidence intervals for predictions have to take into account both the uncertainty of the intercept and the uncertainty of the actual slope). My larger question is: If I want to determine whether a particular species is unusual given some comparative data, and I want to take phylogeny into account when doing this, what is the most legitimate way to proceed? Or is there no general agreement on this point? Thanks for any suggestions, -Tom _ P. Thomas Schoenemann Associate Professor Department of Anthropology Cognitive Science Program Indiana University Bloomington, IN 47405 Phone: 812-855-8800 E-mail: t...@indiana.edu Open Research Scan Archive (ORSA) Co-Director Consulting Scholar Museum of Archaeology and Anthropology University of Pennsylvania http://www.indiana.edu/~brainevo [[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/
Re: [R-sig-phylo] PGLS vs lm
My goal, it seems to me, is to get a bunch of replications of data in which one trait shows a phylogenetic signal, but the other one does not, but also that both share some predefined correlation with each other (over time). I can then test different kinds of methods to see which would be most appropriate statistical method for this kind of problem. I can see how I could simulate traits evolving with a given correlation value over a given tree, using sim.char() in R. However, won't this leave me with traits in which both have the same phylogenetic signal? Is my only option to simulate huge numbers of traits, half of which are evolving consistent with some tree, and the other half are independent of the tree (i.e., random numbers?), and then correlate pairs (one from each of these groups), retaining just those that have the level of correlation I'm interested in exploring? Thanks for any suggestions, -Tom On Jul 26, 2013, at 6:42 PM, Theodore Garland Jr wrote: > Hi Tom, > > So far I have resisted jumping in here, but maybe this will help. > Come up with a model for how you think your traits of interest might evolve > together in a correlated fashion along a phylogenetic tree. > Now implement it in a computer simulation along a phylogenetic tree. > Also implement the model with no correlation between the traits. > Analyze the data with whatever methods you choose. > Check the Type I error rate and then the power of each method. Also check > the bias and means squared error for the parameter you are trying to estimate. > See what method works best. > Use that method for your data if you have some confidence that the model you > used to simulate trait evolution is reasonable, based on your understanding > (and intuition) about the biology involved. > > Lots of us have done this sort of thing, e.g., check this: > > Martins, E. P., and T. Garland, Jr. 1991. Phylogenetic analyses of the > correlated evolution of continuous characters: a simulation study. Evolution > 45:534-557. > > > > Cheers, > Ted > > Theodore Garland, Jr., Professor > Department of Biology > University of California, Riverside > Riverside, CA 92521 > Office Phone: (951) 827-3524 > Wet Lab Phone: (951) 827-5724 > Dry Lab Phone: (951) 827-4026 > Home Phone: (951) 328-0820 > Skype: theodoregarland > Facsimile: (951) 827-4286 = Dept. office (not confidential) > Email: tgarl...@ucr.edu > http://www.biology.ucr.edu/people/faculty/Garland.html > http://scholar.google.com/citations?hl=en&user=iSSbrhwJ > > Inquiry-based Middle School Lesson Plan: > "Born to Run: Artificial Selection Lab" > http://www.indiana.edu/~ensiweb/lessons/BornToRun.html > > From: r-sig-phylo-boun...@r-project.org [r-sig-phylo-boun...@r-project.org] > on behalf of Tom Schoenemann [t...@indiana.edu] > Sent: Friday, July 26, 2013 3:21 PM > To: Tom Schoenemann > Cc: r-sig-phylo@r-project.org > Subject: Re: [R-sig-phylo] PGLS vs lm > > OK, so I haven't gotten any responses that convince me that PGLS isn't > biologically suspect. At the risk of thinking out loud to myself here, I > wonder if my finding might have to do with the method detecting phylogenetic > signal in the error (residuals?): > > From: > Revell, L. J. (2010). Phylogenetic signal and linear regression on species > data. Methods in Ecology and Evolution, 1(4), 319-329. > > I note the following: "...the suitability of a phylogenetic regression should > actually be diagnosed by estimating phylogenetic signal in the residual > deviations of Y given our predictors (X1, X2, etc.)." > > Let's say one variable, "A", has a strong evolutionary signal, but the other, > variable "B", does not. Would we expect this to affect a PGLS differently if > we use A to predict B, vs. using B to predict A? > > If so, it would explain my findings. However, given the difference, I can > have no confidence that there is, or is not, a significant covariance between > A and B independent of phylogeny. Doesn't this finding call into question the > method itself? > > More directly, how is one to interpret such a finding? Is there, or is there > not, a significant biological association? > > -Tom > > > On Jul 21, 2013, at 11:47 PM, Tom Schoenemann wrote: > > > Thanks Liam, > > > > A couple of questions: > > > > How does one do a hypothesis test on a regression, controlling for > > phylogeny, if not using PGLS as I am doing? I realize one could use > > independent contrasts, though I was led to believe that is equivalent to a > > PGLS with lambda = 1. > > > > I take it from what you wrote th
Re: [R-sig-phylo] PGLS vs lm
Thanks for the suggestions. I'll see if I can implement them. However, I'm curious if anyone can address my specific questions: Does it make biological sense for one variable "A" to predict another "B" significantly, but for "B" to predict "A"? -Tom On Jul 26, 2013, at 6:42 PM, Theodore Garland Jr wrote: > Hi Tom, > > So far I have resisted jumping in here, but maybe this will help. > Come up with a model for how you think your traits of interest might evolve > together in a correlated fashion along a phylogenetic tree. > Now implement it in a computer simulation along a phylogenetic tree. > Also implement the model with no correlation between the traits. > Analyze the data with whatever methods you choose. > Check the Type I error rate and then the power of each method. Also check > the bias and means squared error for the parameter you are trying to estimate. > See what method works best. > Use that method for your data if you have some confidence that the model you > used to simulate trait evolution is reasonable, based on your understanding > (and intuition) about the biology involved. > > Lots of us have done this sort of thing, e.g., check this: > > Martins, E. P., and T. Garland, Jr. 1991. Phylogenetic analyses of the > correlated evolution of continuous characters: a simulation study. Evolution > 45:534-557. > > > > Cheers, > Ted > > Theodore Garland, Jr., Professor > Department of Biology > University of California, Riverside > Riverside, CA 92521 > Office Phone: (951) 827-3524 > Wet Lab Phone: (951) 827-5724 > Dry Lab Phone: (951) 827-4026 > Home Phone: (951) 328-0820 > Skype: theodoregarland > Facsimile: (951) 827-4286 = Dept. office (not confidential) > Email: tgarl...@ucr.edu > http://www.biology.ucr.edu/people/faculty/Garland.html > http://scholar.google.com/citations?hl=en&user=iSSbrhwJ > > Inquiry-based Middle School Lesson Plan: > "Born to Run: Artificial Selection Lab" > http://www.indiana.edu/~ensiweb/lessons/BornToRun.html > > From: r-sig-phylo-boun...@r-project.org [r-sig-phylo-boun...@r-project.org] > on behalf of Tom Schoenemann [t...@indiana.edu] > Sent: Friday, July 26, 2013 3:21 PM > To: Tom Schoenemann > Cc: r-sig-phylo@r-project.org > Subject: Re: [R-sig-phylo] PGLS vs lm > > OK, so I haven't gotten any responses that convince me that PGLS isn't > biologically suspect. At the risk of thinking out loud to myself here, I > wonder if my finding might have to do with the method detecting phylogenetic > signal in the error (residuals?): > > From: > Revell, L. J. (2010). Phylogenetic signal and linear regression on species > data. Methods in Ecology and Evolution, 1(4), 319-329. > > I note the following: "...the suitability of a phylogenetic regression should > actually be diagnosed by estimating phylogenetic signal in the residual > deviations of Y given our predictors (X1, X2, etc.)." > > Let's say one variable, "A", has a strong evolutionary signal, but the other, > variable "B", does not. Would we expect this to affect a PGLS differently if > we use A to predict B, vs. using B to predict A? > > If so, it would explain my findings. However, given the difference, I can > have no confidence that there is, or is not, a significant covariance between > A and B independent of phylogeny. Doesn't this finding call into question the > method itself? > > More directly, how is one to interpret such a finding? Is there, or is there > not, a significant biological association? > > -Tom > > > On Jul 21, 2013, at 11:47 PM, Tom Schoenemann wrote: > > > Thanks Liam, > > > > A couple of questions: > > > > How does one do a hypothesis test on a regression, controlling for > > phylogeny, if not using PGLS as I am doing? I realize one could use > > independent contrasts, though I was led to believe that is equivalent to a > > PGLS with lambda = 1. > > > > I take it from what you wrote that the PGLS in caper does a ML of lambda > > only on y, when doing the regression? Isn't this patently wrong, > > biologically speaking? Phylogenetic effects could have been operating on > > both x and y - we can't assume that it would only be relevant to y. > > Shouldn't phylogenetic methods account for both? > > > > You say you aren't sure it is a good idea to jointly optimize lambda for x > > & y. Can you expand on this? What would be a better solution (if there is > > one)? > > > > Am I wrong that it makes no evolutionary biological sense
Re: [R-sig-phylo] PGLS vs lm
OK, so I haven't gotten any responses that convince me that PGLS isn't biologically suspect. At the risk of thinking out loud to myself here, I wonder if my finding might have to do with the method detecting phylogenetic signal in the error (residuals?): From: Revell, L. J. (2010). Phylogenetic signal and linear regression on species data. Methods in Ecology and Evolution, 1(4), 319-329. I note the following: "...the suitability of a phylogenetic regression should actually be diagnosed by estimating phylogenetic signal in the residual deviations of Y given our predictors (X1, X2, etc.)." Let's say one variable, "A", has a strong evolutionary signal, but the other, variable "B", does not. Would we expect this to affect a PGLS differently if we use A to predict B, vs. using B to predict A? If so, it would explain my findings. However, given the difference, I can have no confidence that there is, or is not, a significant covariance between A and B independent of phylogeny. Doesn't this finding call into question the method itself? More directly, how is one to interpret such a finding? Is there, or is there not, a significant biological association? -Tom On Jul 21, 2013, at 11:47 PM, Tom Schoenemann wrote: > Thanks Liam, > > A couple of questions: > > How does one do a hypothesis test on a regression, controlling for phylogeny, > if not using PGLS as I am doing? I realize one could use independent > contrasts, though I was led to believe that is equivalent to a PGLS with > lambda = 1. > > I take it from what you wrote that the PGLS in caper does a ML of lambda only > on y, when doing the regression? Isn't this patently wrong, biologically > speaking? Phylogenetic effects could have been operating on both x and y - we > can't assume that it would only be relevant to y. Shouldn't phylogenetic > methods account for both? > > You say you aren't sure it is a good idea to jointly optimize lambda for x & > y. Can you expand on this? What would be a better solution (if there is > one)? > > Am I wrong that it makes no evolutionary biological sense to use a method > that gives different estimates of the probability of a relationship based on > the direction in which one looks at the relationship? Doesn't the fact that > the method gives different answers in this way invalidate the method for > taking phylogeny into account when assessing relationships among biological > taxa? How could it be biologically meaningful for phylogeny to have a > greater influence when x is predicting y, than when y is predicting x? Maybe > I'm missing something here. > > -Tom > > > On Jul 21, 2013, at 8:59 PM, Liam J. Revell wrote: > >> Hi Tom. >> >> Joe pointed out that if we assume that our variables are multivariate >> normal, then a hypothesis test on the regression is the same as a test that >> cov(x,y) is different from zero. >> >> If you insist on using lambda, one logical extension to this might be to >> jointly optimize lambda for x & y (following Freckleton et al. 2002) and >> then fix the value of lambda at its joint MLE during GLS. This would at >> least have the property of guaranteeing that the P-values for y~x and x~y >> are the same >> >> I previously posted code for joint estimation of lambda on my blog here: >> http://blog.phytools.org/2012/09/joint-estimation-of-pagels-for-multiple.html. >> >> With this code to fit joint lambda, our analysis would then look something >> like this: >> >> require(phytools) >> require(nlme) >> lambda<-joint.lambda(tree,cbind(x,y))$lambda >> fit1<-gls(y~x,data=data.frame(x,y),correlation=corPagel(lambda,tree,fixed=TRUE)) >> fit2<-gls(x~y,data=data.frame(x,y),correlation=corPagel(lambda,tree,fixed=TRUE)) >> >> I'm not sure that this is a good idea - but it is possible >> >> - Liam >> >> Liam J. Revell, Assistant 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 7/21/2013 6:15 PM, Tom Schoenemann wrote: >>> Hi all, >>> >>> I'm still unsure of how I should interpret results given that using PGLS >>> to predict group size from brain size gives different significance >>> levels and lambda estimates than when I do the reverse (i.e., predict >>> brain size from group size). Biologically, I don't think this makes any >>> sense. If lambda is an estimate of the phylogenetic signal, what >>> possible evolutiona
Re: [R-sig-phylo] PGLS vs lm
Dear Santiago, I agree that evolving traits might have all sorts of complicated relationships, but that doesn't mean we shouldn't rule out simple relationships first. And besides, the most basic question one can ask - really the first question to ask - is whether there is any association at all between two variables. If we are trying to find out if such an association exists, independent of phylogeny, then we need a method that gives the same results regardless of whether which variable we look at. Of course the slope of any relationship will be different, depending on whether we are trying to predict x from y, or y from x. But that shouldn't biologically affect the covariance between the two variables. The covariance by definition is not a measure of x specifically from y, or vice-versa, it is a measure of how they both covary (there is no directionality to this). So any method that suggests one degree of confidence in this covariance if we look at x from y, and a different degree of confidence if we look at y from x, is simply not biologically valid for assessing covariance. To put it in the context of brain and group size: Is group size covarying significantly with brain size or not? Well, if you try to predict group size from brain size, then PGLS says the confidence we should have of this covariance is higher than if you try to predict brain size from group size. This makes no biological sense, and I maintain this makes PGLS invalid for assessing the significance of covariance between two variables. -Tom On Jul 22, 2013, at 2:02 AM, Santiago Claramunt wrote: > Dear Tom, > > If your concept of 'relationship' is a simple correlation analysis, then it > may not make sense to get different estimates of the 'probability of the > relationship'. But in evolutionary biology things are always more complicated > than a simple correlation model. Things are not linear, causality is > indirect, and, yes, observations are not independent because of phylogen (and > space). We clearly need methods that are more sophisticated than a simple > correlation analysis. > > Brain size and groups size are variables of very different nature, and their > relationship may be the product of natural selection acting on lineages over > evolutionary time, which form phylogenies. I don't see any problem in > obtaining somewhat different results depending on how the relationship is > modeled. > > Santiago > > > On Jul 21, 2013, at 11:47 PM, Tom Schoenemann wrote: > >> Thanks Liam, >> >> A couple of questions: >> >> How does one do a hypothesis test on a regression, controlling for >> phylogeny, if not using PGLS as I am doing? I realize one could use >> independent contrasts, though I was led to believe that is equivalent to a >> PGLS with lambda = 1. >> >> I take it from what you wrote that the PGLS in caper does a ML of lambda >> only on y, when doing the regression? Isn't this patently wrong, >> biologically speaking? Phylogenetic effects could have been operating on >> both x and y - we can't assume that it would only be relevant to y. >> Shouldn't phylogenetic methods account for both? >> >> You say you aren't sure it is a good idea to jointly optimize lambda for x & >> y. Can you expand on this? What would be a better solution (if there is >> one)? >> >> Am I wrong that it makes no evolutionary biological sense to use a method >> that gives different estimates of the probability of a relationship based on >> the direction in which one looks at the relationship? Doesn't the fact that >> the method gives different answers in this way invalidate the method for >> taking phylogeny into account when assessing relationships among biological >> taxa? How could it be biologically meaningful for phylogeny to have a >> greater influence when x is predicting y, than when y is predicting x? >> Maybe I'm missing something here. >> >> -Tom >> >> >> On Jul 21, 2013, at 8:59 PM, Liam J. Revell wrote: >> >>> Hi Tom. >>> >>> Joe pointed out that if we assume that our variables are multivariate >>> normal, then a hypothesis test on the regression is the same as a test that >>> cov(x,y) is different from zero. >>> >>> If you insist on using lambda, one logical extension to this might be to >>> jointly optimize lambda for x & y (following Freckleton et al. 2002) and >>> then fix the value of lambda at its joint MLE during GLS. This would at >>> least have the property of guaranteeing that the P-values for y~x and x~y >>> are the sa
Re: [R-sig-phylo] PGLS vs lm
Thanks Liam, A couple of questions: How does one do a hypothesis test on a regression, controlling for phylogeny, if not using PGLS as I am doing? I realize one could use independent contrasts, though I was led to believe that is equivalent to a PGLS with lambda = 1. I take it from what you wrote that the PGLS in caper does a ML of lambda only on y, when doing the regression? Isn't this patently wrong, biologically speaking? Phylogenetic effects could have been operating on both x and y - we can't assume that it would only be relevant to y. Shouldn't phylogenetic methods account for both? You say you aren't sure it is a good idea to jointly optimize lambda for x & y. Can you expand on this? What would be a better solution (if there is one)? Am I wrong that it makes no evolutionary biological sense to use a method that gives different estimates of the probability of a relationship based on the direction in which one looks at the relationship? Doesn't the fact that the method gives different answers in this way invalidate the method for taking phylogeny into account when assessing relationships among biological taxa? How could it be biologically meaningful for phylogeny to have a greater influence when x is predicting y, than when y is predicting x? Maybe I'm missing something here. -Tom On Jul 21, 2013, at 8:59 PM, Liam J. Revell wrote: > Hi Tom. > > Joe pointed out that if we assume that our variables are multivariate normal, > then a hypothesis test on the regression is the same as a test that cov(x,y) > is different from zero. > > If you insist on using lambda, one logical extension to this might be to > jointly optimize lambda for x & y (following Freckleton et al. 2002) and then > fix the value of lambda at its joint MLE during GLS. This would at least have > the property of guaranteeing that the P-values for y~x and x~y are the > same > > I previously posted code for joint estimation of lambda on my blog here: > http://blog.phytools.org/2012/09/joint-estimation-of-pagels-for-multiple.html. > > With this code to fit joint lambda, our analysis would then look something > like this: > > require(phytools) > require(nlme) > lambda<-joint.lambda(tree,cbind(x,y))$lambda > fit1<-gls(y~x,data=data.frame(x,y),correlation=corPagel(lambda,tree,fixed=TRUE)) > fit2<-gls(x~y,data=data.frame(x,y),correlation=corPagel(lambda,tree,fixed=TRUE)) > > I'm not sure that this is a good idea - but it is possible > > - Liam > > Liam J. Revell, Assistant 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 7/21/2013 6:15 PM, Tom Schoenemann wrote: >> Hi all, >> >> I'm still unsure of how I should interpret results given that using PGLS >> to predict group size from brain size gives different significance >> levels and lambda estimates than when I do the reverse (i.e., predict >> brain size from group size). Biologically, I don't think this makes any >> sense. If lambda is an estimate of the phylogenetic signal, what >> possible evolutionary and biological sense are we to make if the >> estimates of lambda are significantly different depending on which way >> the association is assessed? I understand the mathematics may allow >> this, but if I can't make sense of this biologically, then doesn't it >> call into question the use of this method for these kinds of questions >> in the first place? What am I missing here? >> >> Here is some results from data I have that illustrate this (notice that >> the lambda values are significantly different from each other): >> >> Group size predicted by brain size: >> >>> model.group.by.brain<-pgls(log(GroupSize) ~ log(AvgBrainWt), data = >>> primate_tom, lambda='ML') >>> summary(model.group.by.brain) >> >> Call: >> pgls(formula = log(GroupSize) ~ log(AvgBrainWt), data = primate_tom, >> lambda = "ML") >> >> Residuals: >> Min 1Q Median 3Q Max >> -0.27196 -0.07638 0.00399 0.10107 0.43852 >> >> Branch length transformations: >> >> kappa [Fix] : 1.000 >> lambda [ ML] : 0.759 >>lower bound : 0.000, p = 4.6524e-08 >>upper bound : 1.000, p = 2.5566e-10 >>95.0% CI : (0.485, 0.904) >> delta [Fix] : 1.000 >> >> Coefficients: >> Estimate Std. Error t value Pr(>|t|) >> (Intercept) -0.080099 0.610151 -0.1313 0.895825 >> log(AvgBrainWt) 0.483366 0.136694 3.5361 0.000622 *** >
Re: [R-sig-phylo] PGLS vs lm
t least to me): testing whether group size affects brain size or the > opposite (or both) is an important question. There's been also a lot of > debate whether comparative data can answer this question. Maybe what we need > here is an approach based on simultaneous equations (aka structural equation > models), but I'm not aware whether this exists in a phylogenetic framework. > The approach by Hansen and Bartoszek could be a step in this direction. > > Best, > > Emmanuel > > Le 13/07/2013 02:59, Joe Felsenstein a écrit : >> >> Tom Schoenemann asked me: >> >>> With respect to your crankiness, is this the paper by Hansen that you are >>> referring to?: >>> >>> Bartoszek, K., Pienaar, J., Mostad, P., Andersson, S., & Hansen, T. F. >>> (2012). A phylogenetic comparative method for studying multivariate >>> adaptation. Journal of Theoretical Biology, 314(0), 204-215. >>> >>> I wrote Bartoszek to see if I could get his R code to try the method >>> mentioned in there. If I can figure out how to apply it to my data, that >>> will be great. I agree that it is clearly a mistake to assume one variable >>> is responding evolutionarily only to the current value of the other >>> (predictor variables). >> >> I'm glad to hear that *somebody* here thinks it is a mistake (because it >> really is). I keep mentioning it here, and Hansen has published extensively >> on it, but everyone keeps saying "Well, my friend used it, and he got >> tenure, so it must be OK". >> >> The paper I saw was this one: >> >> Hansen, Thomas F & Bartoszek, Krzysztof (2012). Interpreting the >> evolutionary regression: The interplay between observational and biological >> errors in phylogenetic comparative studies. Systematic Biology 61 (3): >> 413-425. ISSN 1063-5157. >> >> J.F. >> >> Joe Felsenstein j...@gs.washington.edu >> Department of Genome Sciences and Department of Biology, >> University of Washington, Box 355065, Seattle, WA 98195-5065 USA >> >> ___ >> 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/ >> _ P. Thomas Schoenemann Associate Professor Department of Anthropology Cognitive Science Program Indiana University Bloomington, IN 47405 Phone: 812-855-8800 E-mail: t...@indiana.edu Open Research Scan Archive (ORSA) Co-Director Consulting Scholar Museum of Archaeology and Anthropology University of Pennsylvania http://www.indiana.edu/~brainevo [[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/
Re: [R-sig-phylo] PGLS vs lm
Thanks Liam, OK, I'm starting to understand this better. But I'm not sure what now to do. Given that the mathematics are such that a PGLS gives significance in one direction, but not in another, what is the most convincing way to show that the two variables really ARE associated (at some level of probability) independent of phylogeny? Ultimately I want to investigate the following: Given 2 (or more) behavioral measures, what is the probability that they are independently associated with brain size in my sample, controlling for phylogeny. I'd also like to create a prediction model that allows me to estimate what the behavioral values would be for a given brain size (of course with confidence intervals, so I could assess whether the model is really actually useful at all for prediction). Thanks for any suggestions, -Tom On Jul 11, 2013, at 5:23 PM, Liam J. Revell wrote: > Hi Tom. > > This is actually not a property of GLS - but of using different correlation > structures when fitting y~x vs. x~y. When you set > correlation=corPagel(...,fixed=FALSE) (the default for corPagel), gls will > fit Pagel's lambda model to the residual error in y|x. The fitted value of > lambda will almost always be different between y|x and x|y. Since the fitted > correlation structure of the residual error is used to calculate our standard > error for beta, this will affect any hypothesis test about beta. > > By contrast, if we assume a fixed error structure (OLS, as in lm; or > correlation=corBrownian(...) - the latter being the same as contrasts > regression), we will find that the P values are the same for y~x vs. x~y. > > library(phytools) > library(nlme) > tree<-pbtree(n=100) > x<-fastBM(tree) > # note I have intentionally simulated y without phylogenetic signal > y<-setNames(rnorm(n=100),names(x)) > fit.a<-gls(y~x,data.frame(x,y),correlation=corBrownian(1,tree)) > summary(fit.a) > fit.b<-gls(x~y,data.frame(x,y),correlation=corBrownian(1,tree)) > summary(fit.b) > # fit.a & fit.b should have the same P-values > fit.c<-gls(y~x,data.frame(x,y),correlation=corPagel(1,tree)) > summary(fit.c) > fit.d<-gls(x~y,data.frame(x,y),correlation=corPagel(1,tree)) > summary(fit.d) > # fit.c & fit.d will most likely have different P-values > > All the best, Liam > > Liam J. Revell, Assistant 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 7/11/2013 12:03 AM, Tom Schoenemann wrote: >> Hi all, >> >> I ran a PGLS with two variables, call them VarA and VarB, using a >> phylogenetic tree and corPagel. When I try to predict VarA from VarB, I get >> a significant coefficient for VarB. However, if I invert this and try to >> predict VarB from VarA, I do NOT get a significant coefficient for VarA. >> Shouldn't these be both significant, or both insignificant (the actual >> outputs and calls are pasted below)? >> >> If I do a simple lm for these, I get the same significance level for the >> coefficients either way (i.e., lm(VarA ~ VarB) vs. lm(VarB ~ VarA), though >> the values of the coefficients of course differ. >> >> Can someone help me understand why the PGLS would not necessarily be >> symmetric in this same way? >> >> Thanks, >> >> -Tom >> >>> outTree_group_by_brain_LambdaEst_redo1 <- gls(log_group_size_data ~ >>> log_brain_weight_data, correlation = bm.t.100species_lamEst_redo1,data = >>> DF.brain.repertoire.group, method= "ML") >>> summary(outTree_group_by_brain_LambdaEst_redo1) >> Generalized least squares fit by maximum likelihood >> Model: log_group_size_data ~ log_brain_weight_data >> Data: DF.brain.repertoire.group >>AIC BIClogLik >> 89.45152 99.8722 -40.72576 >> Correlation Structure: corPagel >> Formula: ~1 >> Parameter estimate(s): >>lambda >> 0.7522738 >> Coefficients: >>Value Std.Error t-value p-value >> (Intercept) -0.0077276 0.2628264 -0.029402 0.9766 >> log_brain_weight_data 0.4636859 0.1355499 3.420778 0.0009 >> >> Correlation: >> (Intr) >> log_brain_weight_data -0.637 >> Standardized residuals: >>Min Q1Med Q3Max >> -1.7225003 -0.1696079 0.5753531 1.0705308 3.0685637 >> Residual standard error: 0.5250319 >> Degrees of freedom: 100 total; 98 residual >> >> >> Here is the inverse: >> >>> outTree_brain_by_grou
Re: [R-sig-phylo] PGLS vs lm
With respect to your crankiness, is this the paper by Hansen that you are referring to?: Bartoszek, K., Pienaar, J., Mostad, P., Andersson, S., & Hansen, T. F. (2012). A phylogenetic comparative method for studying multivariate adaptation. Journal of Theoretical Biology, 314(0), 204-215. I wrote Bartoszek to see if I could get his R code to try the method mentioned in there. If I can figure out how to apply it to my data, that will be great. I agree that it is clearly a mistake to assume one variable is responding evolutionarily only to the current value of the other (predictor variables). Regarding your comments: > If the "regressions" are being done in a model which implies > that the two variables are multivariate normal, then we can > simply estimate the parameters of that joint distribution, > which are of course the two means and the three elements of the > covariance matrix. > > If we then test whether Cov(X,Y) is different from zero, that > should be equivalent to a test of significance of either > regression. I'm not clear on what you are suggesting I do here. Isn't PGLS essentially testing Cov(X,Y) taking the phylogeny into account? And are you saying there is a way to show that my variables are significantly associated with each other even though PGLS shows different things depending on which way I run the associations? -Tom On Jul 11, 2013, at 5:46 PM, Joe Felsenstein wrote: > > If the "regressions" are being done in a model which implies > that the two variables are multivariate normal, then we can > simply estimate the parameters of that joint distribution, > which are of course the two means and the three elements of the > covariance matrix. > > If we then test whether Cov(X,Y) is different from zero, that > should be equivalent to a test of significance of either > regression. > > /* crankiness on */ > Note of course that most "phylogenetic" regressions are being > done wrong: if they assume that Y responds to the current value > of X, but when the value of Y may actually be the result of > optimum selection which is affected by past values of X which > we do not observe directly. > > I've complained about this here in the past, to no avail, > Thomas Hansen, in a recent paper, made the same point, with > evidence too. > /* crankiness off */ > > J.F. > > Joe Felsenstein j...@gs.washington.edu > Department of Genome Sciences and Department of Biology, > University of Washington, Box 355065, Seattle, WA 98195-5065 USA _ P. Thomas Schoenemann Associate Professor Department of Anthropology Cognitive Science Program Indiana University Bloomington, IN 47405 Phone: 812-855-8800 E-mail: t...@indiana.edu Open Research Scan Archive (ORSA) Co-Director Consulting Scholar Museum of Archaeology and Anthropology University of Pennsylvania http://www.indiana.edu/~brainevo [[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/
Re: [R-sig-phylo] PGLS vs lm
OK, I started going through the Ives et al. paper - thanks for that. Note that my data is not brain size vs. body size, but brain size vs. social group size (not a measure for which brain size is a subset). For our particular dataset, I believe we were not able to find much in the way of within-species variation for one of the variables - typically one report per species, and usually no variation given (but I'm not sure on that - I'll have to check). Regarding what exactly we want to do: 1) is there a significant association between brain size and two other behavioral dimensions (reported in the literature), after taking into account (as best we can) phylogeny. This is why I was trying PGLS. We probably also want to look at the relationship within clades (is there a phylogenetically appropriate version of ANCOVA?). 2) are these two other behavioral measures independently associated with brain size (after controlling for the other) - I'm assuming this would be a phylogenetically appropriate version of multiple regression But my issue is that, if I use PGLS, I get significant coefficients if I do it one direction, and not in the other. This makes me skeptical that there is a significant association in the first place. -Tom On Jul 11, 2013, at 4:32 PM, Theodore Garland Jr wrote: > I think the issue is largely one of conceptualizing the problem. > People often view body size as an "independent variable" when analyzing brain > size, but obviously this is a serious oversimplificaiton -- usually done for > statistical convenience -- that does not reflect the biology (yes, I have > also done this!). Moreover, brain mass is part of body mass, so if you use > body mass per se as an independent variable then you have potential > part-whole correlation statistical issues. > > I would think carefully about what you are really wanting to do (e.g., > regression vs. correlation vs. ANCOVA), and check over this paper: > > Ives, A. R., P. E. Midford, and T. Garland, Jr. 2007. Within-species > variation and measurement error in phylogenetic comparative methods. > Systematic Biology 56:252-270. > > > And maybe this one: > > Garland, T., Jr., A. W. Dickerman, C. M. Janis, and J. A. Jones. 1993. > Phylogenetic analysis of covariance by computer simulation. Systematic > Biology 42:265-292. > > > Cheers, > Ted > > Theodore Garland, Jr., Professor > Department of Biology > University of California, Riverside > Riverside, CA 92521 > Office Phone: (951) 827-3524 > Wet Lab Phone: (951) 827-5724 > Dry Lab Phone: (951) 827-4026 > Home Phone: (951) 328-0820 > Skype: theodoregarland > Facsimile: (951) 827-4286 = Dept. office (not confidential) > Email: tgarl...@ucr.edu > http://www.biology.ucr.edu/people/faculty/Garland.html > http://scholar.google.com/citations?hl=en&user=iSSbrhwJ > > Inquiry-based Middle School Lesson Plan: > "Born to Run: Artificial Selection Lab" > http://www.indiana.edu/~ensiweb/lessons/BornToRun.html > > From: r-sig-phylo-boun...@r-project.org [r-sig-phylo-boun...@r-project.org] > on behalf of Tom Schoenemann [t...@indiana.edu] > Sent: Thursday, July 11, 2013 11:19 AM > To: Emmanuel Paradis > Cc: r-sig-phylo@r-project.org > Subject: Re: [R-sig-phylo] PGLS vs lm > > Thanks Emmanuel, > > OK, so this makes sense in terms of the math involved. However, from a > practical, interpretive perspective, shouldn't I assume this to mean that we > actually cannot say (from this data) whether VarA and VarB ARE actually > associated with each other? In the real world, if VarA is causally related to > VarB, then by definition they will be associated. Doesn't this type of > situation - where the associations are judged to be statistically significant > in one direction but not in the other - suggest that we actually DON'T have > confidence that - independent of phylogeny - VarA is associated with VarB? > Putting this in the context of the actual variables involved, doesn't this > mean that we actually can't be sure brain size is associated with social > group size (in this dataset) independent of phylogeny? > > I notice that the maximum likelihood lambda estimates are different (though > I'm not sure they are significantly so). I understand this could > mathematically be so, but I'm concerned with how to interpret this. In the > real world, how could phylogenetic relatedness affect group size predicting > brain size, more than brain size predicting group size? Isn't this a logical > problem (for interpretation - not for the math)? In other words, in > evolutionary history, shouldn't phylogeny affect the relationship between two > variables in only one w
Re: [R-sig-phylo] PGLS vs lm
Thanks Emmanuel, OK, so this makes sense in terms of the math involved. However, from a practical, interpretive perspective, shouldn't I assume this to mean that we actually cannot say (from this data) whether VarA and VarB ARE actually associated with each other? In the real world, if VarA is causally related to VarB, then by definition they will be associated. Doesn't this type of situation - where the associations are judged to be statistically significant in one direction but not in the other - suggest that we actually DON'T have confidence that - independent of phylogeny - VarA is associated with VarB? Putting this in the context of the actual variables involved, doesn't this mean that we actually can't be sure brain size is associated with social group size (in this dataset) independent of phylogeny? I notice that the maximum likelihood lambda estimates are different (though I'm not sure they are significantly so). I understand this could mathematically be so, but I'm concerned with how to interpret this. In the real world, how could phylogenetic relatedness affect group size predicting brain size, more than brain size predicting group size? Isn't this a logical problem (for interpretation - not for the math)? In other words, in evolutionary history, shouldn't phylogeny affect the relationship between two variables in only one way, which would show up whichever way we approached the association? Again, I understand the math may allow it, I just don't understand how it could actually be true over evolutionary time. Thanks in advance for helping me understand this better, -Tom On Jul 11, 2013, at 5:12 AM, Emmanuel Paradis wrote: > Hi Tom, > > In an OLS regression, the residuals from both regressions (varA ~ varB and > varB ~ varA) are different but their distributions are (more or less) > symmetric. So, because the residuals are independent (ie, their covariance is > null), the residual standard error will be the same (or very close in > practice). > > In GLS, the residuals are not independent, so this difference in the > distribution of the residuals affects the estimation of the residual standard > errors (because we need to estimate the covaraince of the residuals), and > consequently the associated tests. > > Best, > > Emmanuel > > Le 11/07/2013 11:03, Tom Schoenemann a écrit : >> Hi all, >> >> I ran a PGLS with two variables, call them VarA and VarB, using a >> phylogenetic tree and corPagel. When I try to predict VarA from VarB, I get >> a significant coefficient for VarB. However, if I invert this and try to >> predict VarB from VarA, I do NOT get a significant coefficient for VarA. >> Shouldn't these be both significant, or both insignificant (the actual >> outputs and calls are pasted below)? >> >> If I do a simple lm for these, I get the same significance level for the >> coefficients either way (i.e., lm(VarA ~ VarB) vs. lm(VarB ~ VarA), though >> the values of the coefficients of course differ. >> >> Can someone help me understand why the PGLS would not necessarily be >> symmetric in this same way? >> >> Thanks, >> >> -Tom >> >>> outTree_group_by_brain_LambdaEst_redo1 <- gls(log_group_size_data ~ >>> log_brain_weight_data, correlation = bm.t.100species_lamEst_redo1,data = >>> DF.brain.repertoire.group, method= "ML") >>> summary(outTree_group_by_brain_LambdaEst_redo1) >> Generalized least squares fit by maximum likelihood >> Model: log_group_size_data ~ log_brain_weight_data >> Data: DF.brain.repertoire.group >>AIC BIClogLik >> 89.45152 99.8722 -40.72576 >> Correlation Structure: corPagel >> Formula: ~1 >> Parameter estimate(s): >>lambda >> 0.7522738 >> Coefficients: >>Value Std.Error t-value p-value >> (Intercept) -0.0077276 0.2628264 -0.029402 0.9766 >> log_brain_weight_data 0.4636859 0.1355499 3.420778 0.0009 >> >> Correlation: >> (Intr) >> log_brain_weight_data -0.637 >> Standardized residuals: >>Min Q1Med Q3Max >> -1.7225003 -0.1696079 0.5753531 1.0705308 3.0685637 >> Residual standard error: 0.5250319 >> Degrees of freedom: 100 total; 98 residual >> >> >> Here is the inverse: >> >>> outTree_brain_by_group_LambdaEst_redo1 <- gls(log_brain_weight_data ~ >>> log_group_size_data, correlation = bm.t.100species_lamEst_redo1,data = >>> DF.brain.repertoire.group, method= "ML") >>> summary(outTree_bra
[R-sig-phylo] PGLS vs lm
Hi all, I ran a PGLS with two variables, call them VarA and VarB, using a phylogenetic tree and corPagel. When I try to predict VarA from VarB, I get a significant coefficient for VarB. However, if I invert this and try to predict VarB from VarA, I do NOT get a significant coefficient for VarA. Shouldn't these be both significant, or both insignificant (the actual outputs and calls are pasted below)? If I do a simple lm for these, I get the same significance level for the coefficients either way (i.e., lm(VarA ~ VarB) vs. lm(VarB ~ VarA), though the values of the coefficients of course differ. Can someone help me understand why the PGLS would not necessarily be symmetric in this same way? Thanks, -Tom > outTree_group_by_brain_LambdaEst_redo1 <- gls(log_group_size_data ~ > log_brain_weight_data, correlation = bm.t.100species_lamEst_redo1,data = > DF.brain.repertoire.group, method= "ML") > summary(outTree_group_by_brain_LambdaEst_redo1) Generalized least squares fit by maximum likelihood Model: log_group_size_data ~ log_brain_weight_data Data: DF.brain.repertoire.group AIC BIClogLik 89.45152 99.8722 -40.72576 Correlation Structure: corPagel Formula: ~1 Parameter estimate(s): lambda 0.7522738 Coefficients: Value Std.Error t-value p-value (Intercept) -0.0077276 0.2628264 -0.029402 0.9766 log_brain_weight_data 0.4636859 0.1355499 3.420778 0.0009 Correlation: (Intr) log_brain_weight_data -0.637 Standardized residuals: Min Q1Med Q3Max -1.7225003 -0.1696079 0.5753531 1.0705308 3.0685637 Residual standard error: 0.5250319 Degrees of freedom: 100 total; 98 residual Here is the inverse: > outTree_brain_by_group_LambdaEst_redo1 <- gls(log_brain_weight_data ~ > log_group_size_data, correlation = bm.t.100species_lamEst_redo1,data = > DF.brain.repertoire.group, method= "ML") > summary(outTree_brain_by_group_LambdaEst_redo1) Generalized least squares fit by maximum likelihood Model: log_brain_weight_data ~ log_group_size_data Data: DF.brain.repertoire.group AIC BIC logLik -39.45804 -29.03736 23.72902 Correlation Structure: corPagel Formula: ~1 Parameter estimate(s): lambda 1.010277 Coefficients: Value Std.Error t-value p-value (Intercept) 1.2244133 0.20948634 5.844836 0. log_group_size_data -0.0234525 0.03723828 -0.629796 0.5303 Correlation: (Intr) log_group_size_data -0.095 Standardized residuals: Min Q1Med Q3Max -2.0682836 -0.3859688 1.1515176 1.5908565 3.1163377 Residual standard error: 0.4830596 Degrees of freedom: 100 total; 98 residual _ P. Thomas Schoenemann Associate Professor Department of Anthropology Cognitive Science Program Indiana University Bloomington, IN 47405 Phone: 812-855-8800 E-mail: t...@indiana.edu Open Research Scan Archive (ORSA) Co-Director Consulting Scholar Museum of Archaeology and Anthropology University of Pennsylvania http://www.indiana.edu/~brainevo [[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/
Re: [R-sig-phylo] 3. partial correlation with gls residuals? (Tom Schoenemann)
Thanks Rob and Alejandro, OK, I did as suggested and ran a PGLS with A ~ B + C. I was hoping for some clarification of the actual results. Here is a summary: ** Generalized least squares fit by maximum likelihood Model: variableA ~ variableB + variableC Data: DF.B.A.C AIC BIC logLik -23.49499 -10.46914 16.7475 Correlation Structure: corPagel Formula: ~1 Parameter estimate(s): lambda -0.09862731 Coefficients: Value Std.Error t-value p-value (Intercept) 0.5229794 0.04740728 11.031625 0. variableB 0.2200980 0.05012508 4.390976 0. variableC 0.0620030 0.05128472 1.208996 0.2296 Correlation: (Intr) variableB variableB -0.813 variableC0.362 -0.837 Standardized residuals: Min Q1Med Q3Max -3.2951355 -0.4995948 0.2604608 0.8884995 2.8456205 Residual standard error: 0.2067425 Degrees of freedom: 100 total; 97 residual ** Are the following correct interpretations?: 1) Controlling for the phylogeny I used, variableB is associated with variableA independent of variableC, because the p-value of the beta weight for variableB is highly significant (0.) 2) Controlling for the phylogeny I used, variableC is not significantly associated with variableA independent of variableB, because the p-value of its beta weight is not significant (0.2296) 3) There does not seem to be a significant effect of phylogeny on this relationship, since the ML lambda estimate is: -0.09862731 Also, what exactly are the values listed in the "Correlation" section? Does the -0.837 entry indicate that variableB is correlated negatively with variableC controlling for variableA (and/or my phylogeny)? Regarding my original plan to assess the independent relationship between variables using residuals, the Freckleton paper Alejandro kindly forwarded includes this comment: "Note that to estimate the true slope for the effect of x2 using residual regression one would need to regress the residuals of the regression on y on x1 on the residuals of the regression of x2 on x1 (e.g. see Baltagi 1999, pp. 7274 for elaboration of this)." (p. 544) This was what I remembered about the issue myself, though I haven't kept up on the literature Rob and Alejandro mentioned. However, Rob believes the residuals might not be independent of phylogeny, and that I should do a PGLS on them also. This leads to my next question: what ARE the residuals of the PGLS then, if not also corrected for phylogeny? In the case of my specific data, I see that the residuals from a PGLS of variableA ~ variableB are not identical to the residuals of a simple lm of variableA ~ variableB, so I assume that the phylogeny included in the PGLS is having some effect on the residuals? Or is there another reason for the difference? Thanks for any clarifications! -Tom On Mar 12, 2012, at 8:19 AM, Robert Barton wrote: > > Dear Tom, > > There is no reason to assume that the residuals from your two PGLS analyses > will be independent of phylogeny, so if you are going to do this you should > correlate the residuals phylogenetically (i.e. run them through PGLS). > General problems with using residuals as data have been commented on in the > literature by people like Freckleton, but I think that in the situation > where each variable of interest is regressed on the same confounding > variable it is valid to use residuals - because the correlation between the > residuals is the same as the partial correlation between them. However, the > simplest solution for this analysis would be to regress A on B and C in a > single PGLS. > > Rob Barton > > On 12/03/2012 11:00, "r-sig-phylo-requ...@r-project.org" > wrote: > >> 3. partial correlation with gls residuals? (Tom Schoenemann) > Hello, > > I was hoping to get some feedback on whether I'm doing something legitimate. > Basically, I have 3 variables (say: A, B, and C) measured on 100 species, > and I want to see whether A and B correlate with each other after > controlling for C, and for phylogeny at the same time. > > Here is what I thought seems reasonable: > > 1) do a gls with variable A predicted by variable C, using a corPagel > correlations structure derived from a phylogeny of these species to control > for phylogenetic effects. The residuals from this are then extracted > > 2) do a gls with variable B predicted by variable C, using the same method, > also extracting the residuals for this comparison > > 3) do a simple lm of the residuals from step 1 vs. the residuals from step 2 > > I guess my question is, are the residuals from the gls independent
[R-sig-phylo] partial correlation with gls residuals?
Hello, I was hoping to get some feedback on whether I'm doing something legitimate. Basically, I have 3 variables (say: A, B, and C) measured on 100 species, and I want to see whether A and B correlate with each other after controlling for C, and for phylogeny at the same time. Here is what I thought seems reasonable: 1) do a gls with variable A predicted by variable C, using a corPagel correlations structure derived from a phylogeny of these species to control for phylogenetic effects. The residuals from this are then extracted 2) do a gls with variable B predicted by variable C, using the same method, also extracting the residuals for this comparison 3) do a simple lm of the residuals from step 1 vs. the residuals from step 2 I guess my question is, are the residuals from the gls independent of my phylogeny? If they are, then wouldn't this give me the partial correlation between A and B, controlling for C, and for phylogeny? Or is there a better (or alternative) way to do this? Thanks for any suggestions, -Tom _ P. Thomas Schoenemann Associate Professor Department of Anthropology Indiana University Bloomington, IN 47405 Phone: 812-855-8800 E-mail: t...@indiana.edu Open Research Scan Archive (ORSA) Co-Director Consulting Scholar Museum of Archaeology and Anthropology University of Pennsylvania Homepage: http://mypage.iu.edu/~toms/ [[alternative HTML version deleted]] ___ R-sig-phylo mailing list R-sig-phylo@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-phylo
Re: [R-sig-phylo] problem calculating independent contrasts
Nice! Thanks Graham and Liam, -Tom On Feb 20, 2012, at 9:07 PM, Liam J. Revell wrote: > Graham is absolutely right. If you did this you would find that Chlorocebus > pygerythrus has an underscore separating genus & specific epithet in your > tree, but not in your data vector: > > > require(geiger) > > name.check(tree,x) > $Tree.not.data > [1] "Chlorocebus_pygerythrus" > > $Data.not.tree > [1] "Chlorocebus pygerythrus" > > - Liam > > -- > Liam J. Revell > University of Massachusetts Boston > web: http://faculty.umb.edu/liam.revell/ > email: liam.rev...@umb.edu > blog: http://phytools.blogspot.com > > On 2/20/2012 7:30 PM, Graham Slater wrote: >> Hi Tom, >> >> have you tried running >> >> name.check(t.100species, log_repertoire_data) >> >> to confirm that the names perfectly match in both? If there is even a slight >> mismatch, then pic will ignore all the names in the data and assume that >> they are in the same order as the tip labels in the tree. Thus the pics >> returned would be random. >> >> Graham >> >> Graham Slater >> Department of Ecology and Evolutionary Biology >> University of California, Los Angeles >> 621 Charles E Young Drive South >> Los Angeles >> CA 90095-1606 >> >> (310) 825-4669 >> gsla...@ucla.edu >> www.eeb.ucla.edu/gslater >> >> >> >> >> >> >> On Feb 20, 2012, at 3:54 PM, Tom Schoenemann wrote: >> >>> Hello, >>> >>> I keep getting the following error when trying to calculate independent >>> contrasts: >>> >>>> pic.log_repertoire_data<- pic(log_repertoire_data, t.100species) >>> Warning message: >>> In pic(log_repertoire_data, t.100species) : >>> the names of argument 'x' and the tip labels of the tree did not match: >>> the former were ignored in the analysis. >>> >>> However, unless I'm misunderstanding what this means, then it is not >>> correct. >>> >>> My data is in: >>>> log_repertoire_data >>> Alouatta_palliata Alouatta_seniculus >>> Aotus_nigricepsAotus_trivirgatus >>>0.778151 0.778151 >>> 0.778151 0.778151 >>>Ateles_belzebuth Ateles_fusciceps >>> Ateles_geoffroyi Brachyteles_arachnoides >>>0.778151 0.954243 >>> 1.322219 0.903090 >>> Cacajao_calvusCallicebus_moloch >>> Callicebus_torquatusCallimico_goeldii >>>1.079181 1.041393 >>> 0.845098 0.845098 >>> Callithrix_jacchus Callithrix_penicillata >>> Callithrix_pygmaea Cebus_capucinus >>>0.954243 0.602060 >>> 1.176091 1.414973 >>> Cebus_olivaceus Lagothrix_lagotricha >>> Leontopithecus_rosaliaPithecia_pithecia >>>1.079181 1.146128 >>> 1.00 1.00 >>>Saguinus_fuscicollis Saguinus_geoffroyi >>> Saguinus_midas Saguinus_oedipus >>>1.113943 1.00 >>> 0.903090 0.954243 >>> Saimiri_oerstedii Saimiri_sciureus >>> Cercocebus_torquatus_atys Cercopithecus_ascanius >>>0.602060 1.301030 >>> 1.079181 0.845098 >>> Cercopithecus_campbelli Cercopithecus_cephus >>> Cercopithecus_mitis Cercopithecus_neglectus >>>1.176091 1.204120 >>> 0.845098 0.778151 >>> Cercopithecus_pogonias Chlorocebus_aethiops >>> Colobus_angolensis_palliatus Colobus_guereza >>>1.230449 1.322219 >>> 0.903090 0.845098 >>> Colobus
[R-sig-phylo] problem calculating independent contrasts
Hello, I keep getting the following error when trying to calculate independent contrasts: > pic.log_repertoire_data <- pic(log_repertoire_data, t.100species) Warning message: In pic(log_repertoire_data, t.100species) : the names of argument 'x' and the tip labels of the tree did not match: the former were ignored in the analysis. However, unless I'm misunderstanding what this means, then it is not correct. My data is in: > log_repertoire_data Alouatta_palliata Alouatta_seniculus Aotus_nigricepsAotus_trivirgatus 0.778151 0.778151 0.778151 0.778151 Ateles_belzebuth Ateles_fusciceps Ateles_geoffroyi Brachyteles_arachnoides 0.778151 0.954243 1.322219 0.903090 Cacajao_calvusCallicebus_moloch Callicebus_torquatusCallimico_goeldii 1.079181 1.041393 0.845098 0.845098 Callithrix_jacchus Callithrix_penicillata Callithrix_pygmaea Cebus_capucinus 0.954243 0.602060 1.176091 1.414973 Cebus_olivaceus Lagothrix_lagotricha Leontopithecus_rosaliaPithecia_pithecia 1.079181 1.146128 1.00 1.00 Saguinus_fuscicollis Saguinus_geoffroyi Saguinus_midas Saguinus_oedipus 1.113943 1.00 0.903090 0.954243 Saimiri_oerstedii Saimiri_sciureus Cercocebus_torquatus_atys Cercopithecus_ascanius 0.602060 1.301030 1.079181 0.845098 Cercopithecus_campbelli Cercopithecus_cephus Cercopithecus_mitis Cercopithecus_neglectus 1.176091 1.204120 0.845098 0.778151 Cercopithecus_pogonias Chlorocebus_aethiops Colobus_angolensis_palliatus Colobus_guereza 1.230449 1.322219 0.903090 0.845098 Colobus_polykomos Erythrocebus_patas Lophocebus_albigena Macaca_arctoides 0.903090 1.079181 1.079181 1.230449 Macaca_fascicularis Macaca_mulatta Macaca_nemestrina Macaca_radiata 1.176091 1.204120 1.371068 1.397940 Macaca_silenus Macaca_sylvanus Mandrillus_leucophaeusMandrillus_sphinx 1.322219 1.041393 1.041393 1.00 Miopithecus_talapoin Nasalis_larvatus Papio_anubis Papio_cynocephalus 1.230449 0.698970 1.204120 1.00 Papio_hamadryas Papio_papio Piliocolobus_badius Presbytis_comata 0.477121 1.176091 1.079181 1.041393 Procolobus_verus Semnopithecus_entellus Theropithecus_gelada Trachypithecus_cristatus 0.903090 1.204120 1.342423 1.113943 Trachypithecus_johnii Chlorocebus pygerythrus Gorilla_gorilla_gorilla Hylobates_agilis 1.204120 1.322219 1.361728 0.778151 Hylobates_moloch Pan_paniscus Pan_troglodytes_troglodytes Pongo_abelii 0.954243 1.146128 1.531479 1.505150 Pongo_pygmaeus Arctocebus_calabarensis Avahi_laniger Avahi_occidentalis 1.204120 0.301030 0.477121 0.477121 Cheirogaleus_major Cheirogaleus_medius Daubentonia_madagascariensisEulemur_coronatus 0.477121 0.845098 0.954243 1.00 Eulemur_fulvus_fulvusEulemur_macaco_macaco
