[R-sig-phylo] UK.GeometricMorphometricsUsingRDeanAdams.Jun5-9
Geometric Morphometrics Using R (GMMR01) This course is being delivered by Prof. Dean Adams, Prof. Michael Collyer and Dr. Antigoni Kaliontzopoulou This course will run from 5th - 9th June 2017 at Millport Field centre on the Isle of Cumbre, Scotland. Please note that although the course is held on an island it is extremely accessible and easy to reach using public transport. The field of geometric morphometrics (GM) is concerned with the quantification and analysis of patterns of shape variation, and its covariation with other variables. Over the past several decades these approaches have become a mainstay in the field of ecology, evolutionary biology, and anthropology, and a panoply of analytical tools for addressing specific biological hypotheses concerning shape have been developed. The goal of this is to provide participants with a working knowledge of the theory of geometric morphometrics, as well as practical training in the application of these methods. The course is organized in both theoretical and practical sessions. The theoretical sessions will provide a comprehensive introduction to the methods of landmark-based geometric morphometrics, which aims at providing the participants with a solid theoretical background for understanding the procedures used in shape data analysis. Practical sessions will include worked examples, giving the participants the opportunity to gain hands-on experience in the treatment of shape data using the R package geomorph. These sessions focus on the generation of shape variables from primary landmark data, the statistical treatment of shape variation with respect to biological hypotheses, and the visualization of patterns of shape variation and of the shapes themselves for interpretation of statistical findings, using the R language for statistical programming. While practice datasets will be available, it is strongly recommended that participants come with their own datasets. Note: Because this is a geometric morphometrics workshop in R, it is required that participants have some working knowledge in R. The practical sessions of the course will focus on GM-based analyses, and not basic R user-interfacing. It is therefore strongly recommended that participants refresh their R skills prior to attending the workshop. Course cost is £520 for students and academic staff and £630 for people working in industry. Accommodation package available for £275, includes all meals and refreshments. Course Programme Sunday 5th Meet at Millport field centre at approximately 18:30. Monday 6th – Classes from 09:00 to 18:001: 1: Morphometrics: History, Introduction and Data Types 2: Review of matrix algebra and multivariate statistics 3: Superimposition 4: Software demonstration and lab practicum Tuesday 7th – Classes from 09:00 to 18:00 1: Shape spaces, shape variables, PCA 2: GPA with semi-landmarks 3: Shape covariation 4: Software demonstration and lab practicum Wednesday 8th – Classes from 09:00 to 18:00 1: Phylogenetic shape variation 2: Group Differences & Trajectory Analysis 3: Allometry 4: Software demonstration and lab practicum Thursday 9th – Classes from 09:00 to 18:00 1: Assymetry 2: Missing Data 3: Integration and Modularity 4: Disparity 5: Software demonstration and lab practicum Friday 10th – Classes from 09:00 to 16:00 1: Future Directions 2: Lab Pacticum 3: Student Presentations Please send inquiries to oliverhoo...@prstatistics.com or visit the website www.prstatistics.com Please feel free to distribute this information anywhere you think suitable Upcoming courses - email for details oliverhoo...@prstatistics.com 1. STABLE ISOTOPE MIXING MODELS USING SIAR, SIBER AND MIXSIAR #SIMM 28th Feb – 3rd Mar 2017, Scotland, Dr. Andrew Parnell, Dr. Andrew Jackson http://www.prstatistics.com/course/stable-isotope-mixing-models-using-r-simm03/ 2. NETWORK ANAYLSIS FOR ECOLOGISTS USING R #NTWA 6th – 10th March 2017, Scotland, Dr. Marco Scotti http://www.prstatistics.com/course/network-analysis-ecologists-ntwa01/ 3. ADVANCES IN MULTIVARIATE ANALYSIS OF SPATIAL ECOLOGICAL DATA USING R #MVSP 3rd – 7th April 2017, Scotland, Prof. Pierre Legendre, Dr. Olivier Gauthier http://www.prstatistics.com/course/advances-in-spatial-analysis-of-multivariate-ecological-data-theory-and-practice-mvsp02/ 4. ADVANCING IN STATISTICAL MODELLING FOR EVOLUTIONARY BIOLOGISTS AND ECOLOGISTS USING R #ADVR 17th – 21st April 2017, Scotland, Dr. Luc Bussiere, Dr. Ane Timenes Laugen http://www.prstatistics.com/course/advancing-statistical-modelling-using-r-advr06/ 5. CODING, DATA MANAGEMENT AND SHINY APPLICATIONS USING RSTUDIO FOR EVOLUTIONARY BIOLOGISTS AND ECOLOGISTS #CDSR 15th - 19th May, Scotland Dr. Aline Quadros http://www.prstatistics.com/course/coding-data-management-and-shiny-applications-using-rstudio-for-evolutionary-biologists-and-ecologists-cdsr01/ 6. GEOMETRIC MORPHOMETRICS USING R #GMMR 5th – 9th June 2017,
Re: [R-sig-phylo] Plotting uncertainty in continuous ASR
Hi Kevin. I was thinking the same thing & this is already in phytools on GitHub: http://blog.phytools.org/2017/02/function-to-add-error-bars-to-contmap.html. Thanks for the suggestion. 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 2/28/2017 4:30 AM, Arbuckle, Kevin wrote: Hi Liam, Absolutely fantastic, I can see this being useful on multiple occasions, particularly as it takes another step towards being more explicit about our uncertainty in ASRs for continuous traits (especially when traitgrams are not ideal for whatever reason). If you get time I would suggest turning that code into a function to add to phytools as it might be broadly useful for many researchers, but for my own purposes the code you have added to the blog is perfectly fine. Best wishes, Kev From: Liam J. Revell [liam.rev...@umb.edu] Sent: 27 February 2017 21:57 To: Arbuckle, Kevin; R phylo list Subject: Re: [R-sig-phylo] Plotting uncertainty in continuous ASR Hi Kevin. I now show one way to do this here: http://blog.phytools.org/2017/02/more-on-adding-error-bars-to-contmap.html. 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 2/27/2017 2:32 PM, Arbuckle, Kevin wrote: Hi Liam, Thank for that, definitely much quicker than I could have figured it out. The only other thing I'd do would be have the length varying to reflect the range of the estimates, giving an immediate visual display of both the range of variation and the actual values encompassed, but that's something that can be tailored relatively easily from that code so thanks again. Kev From: Liam J. Revell [liam.rev...@umb.edu] Sent: 27 February 2017 19:13 To: Arbuckle, Kevin; r-sig-phylo@r-project.org Subject: Re: [R-sig-phylo] Plotting uncertainty in continuous ASR Hi Kevin. This is not automatic - but it is indeed fairly easy to do using phytools. To be honest, I thought this would look terrible - but it actually looks much better than I expected, at least for a relatively small tree. I have posted a demo to my blog here: http://blog.phytools.org/2017/02/adding-colorful-error-bars-to-internal.html. 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 2/27/2017 11:16 AM, Arbuckle, Kevin wrote: Hi all, I was wondering about plotting the output of an ancestral state 'reconstruction' of a continuous trait while incorporating at least some of the uncertainty around the estimates. One approach I thought of was to map the ASR onto a tree in a standard way, then at each node have essentially a mini-legend that is of a length reflecting the width of the confidence interval of the estimate at that node, and is coloured on the same colour-scale as the overall tree legend. For instance, if the colour scheme for the tree goes from blue through yellow to red as the value increases, then a node with a relatively precise and high estimate will have a short bar only ranging through different shades of red, whereas a highly uncertain low estimate will have a wider bar coloured from (say) dark blue to orange/light red. I hope that description makes sense. I was wondering if anyone is aware of a function that already implements such an approach, otherwise I'll try to put one together myself. I am aware of phytool's fancyTree(type="phenogram95") as a way of incorporating uncertainty into a plotted ASR for continuous traits. However, this often results in difficulty in distinguishing different nodes where estimates are similar and also does not lend itself easily to, for instance, plotting pie charts representing discrete trait ASRs onto a tree mapped with a continuous trait. Hence I can imagine a more general approach as above but don't want to duplicate effort if a function already exists (and also if others feel this is a useful idea it can be added to existing packages If I share it as above). Best wishes, Kev ___ 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/ ___ 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/
