andrew_gelman_stats andrew_gelman_stats-2014 andrew_gelman_stats-2014-2360 knowledge-graph by maker-knowledge-mining
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Introduction: Andrew Tanentzap, William Lee, Adrian Monks, Kate Ladley, Peter Johnson, Geoffrey Rogers, Joy Comrie, Dean Clarke, and Ella Hayman write : We tested a multivariate hypothesis about the causal mechanisms underlying plant invasions in an ephemeral wetland in South Island, New Zealand to inform management of this biodiverse but globally imperilled habitat. . . . We found that invasion by non-native plants was lowest in sites where the physical disturbance caused by flooding was both intense and frequent. . . . only species adapted to the dominant disturbance regimes at a site may become successful invaders. Their keywords are: causal networks; community dynamics; functional traits, invasive species, kettlehole; megafauna; rabbits; restoration; turf plants But here’s the part that I like best: We fitted all our models within a hierarchical Bayesian framework using . . . STAN v.1.3 (Stan Development Team 2012) from R v.2.15 (R Development Core Team 2012).
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1 We found that invasion by non-native plants was lowest in sites where the physical disturbance caused by flooding was both intense and frequent. [sent-5, score-1.295]
2 only species adapted to the dominant disturbance regimes at a site may become successful invaders. [sent-9, score-0.949]
3 Their keywords are: causal networks; community dynamics; functional traits, invasive species, kettlehole; megafauna; rabbits; restoration; turf plants But here’s the part that I like best: We fitted all our models within a hierarchical Bayesian framework using . [sent-10, score-1.014]
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Introduction: Andrew Tanentzap, William Lee, Adrian Monks, Kate Ladley, Peter Johnson, Geoffrey Rogers, Joy Comrie, Dean Clarke, and Ella Hayman write : We tested a multivariate hypothesis about the causal mechanisms underlying plant invasions in an ephemeral wetland in South Island, New Zealand to inform management of this biodiverse but globally imperilled habitat. . . . We found that invasion by non-native plants was lowest in sites where the physical disturbance caused by flooding was both intense and frequent. . . . only species adapted to the dominant disturbance regimes at a site may become successful invaders. Their keywords are: causal networks; community dynamics; functional traits, invasive species, kettlehole; megafauna; rabbits; restoration; turf plants But here’s the part that I like best: We fitted all our models within a hierarchical Bayesian framework using . . . STAN v.1.3 (Stan Development Team 2012) from R v.2.15 (R Development Core Team 2012).
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Introduction: Stan 1.0.0 and RStan 1.0.0 It’s official. The Stan Development Team is happy to announce the first stable versions of Stan and RStan. What is (R)Stan? Stan is an open-source package for obtaining Bayesian inference using the No-U-Turn sampler, a variant of Hamiltonian Monte Carlo. It’s sort of like BUGS, but with a different language for expressing models and a different sampler for sampling from their posteriors. RStan is the R interface to Stan. Stan Home Page Stan’s home page is: http://mc-stan.org/ It links everything you need to get started running Stan from the command line, from R, or from C++, including full step-by-step install instructions, a detailed user’s guide and reference manual for the modeling language, and tested ports of most of the BUGS examples. Peruse the Manual If you’d like to learn more, the Stan User’s Guide and Reference Manual is the place to start.
3 0.083112106 1102 andrew gelman stats-2012-01-06-Bayesian Anova found useful in ecology
Introduction: David LeBauer points me to this article in PLoS One by Andy Hector, Thomas Bell, Yann Hautier, Forest Isbell, Marc Kéry, Peter Reich, Jasper van Ruijven, and Bernhard Schmid. Here’s the abstract: The idea that species diversity can influence ecosystem functioning has been controversial and its importance relative to compositional effects hotly debated. Unfortunately, assessing the relative importance of different explanatory variables in complex linear models is not simple. In this paper we assess the relative importance of species richness and species composition in a multilevel model analysis of net aboveground biomass production in grassland biodiversity experiments by estimating variance components for all explanatory variables. We compare the variance components using a recently introduced graphical Bayesian ANOVA [emphasis added]. We show that while the use of test statistics and the R2 gives contradictory assessments, the variance components analysis reveals that species
4 0.080303058 2291 andrew gelman stats-2014-04-14-Transitioning to Stan
Introduction: Kevin Cartier writes: I’ve been happily using R for a number of years now and recently came across Stan. Looks big and powerful, so I’d like to pick an appropriate project and try it out. I wondered if you could point me to a link or document that goes into the motivation for this tool (aside from the Stan user doc)? What I’d like to understand is, at what point might you look at an emergent R project and advise, “You know, that thing you’re trying to do would be a whole lot easier/simpler/more straightforward to implement with Stan.” (or words to that effect). My reply: For my collaborators in political science, Stan has been most useful for models where the data set is not huge (e.g., we might have 10,000 data points or 50,000 data points but not 10 million) but where the model is somewhat complex (for example, a model with latent time series structure). The point is that the model has enough parameters and uncertainty that you’ll want to do full Bayes (rather than some sort
5 0.079899155 1061 andrew gelman stats-2011-12-16-CrossValidated: A place to post your statistics questions
Introduction: Seth Rogers writes: I [Rogers] am a member of an online community of statisticians where I burn a great deal of time (and a recovering cog sci researcher). Our community website is a peer-reviewed Q and A spanning stats topics ranging from applications to mathematical theory. Our online community consists of mostly university faculty, grad students and technical consultants. The answer quality is very strong and the web design is intuitive. I think you and your readers are like-minded and would be really interested in some of the topics on the site, CrossValidated (you may know the sister site: stackoverflow.com ). The philosophy is purely to further knowledge for the sake of knowledge and take pride in learning. I took a quick look and the site seemed like it could be useful to people. The only thing I didn’t understand is, why doesn’t it have a search function? (Or maybe it was there somewhere and I couldn’t find it.) P.S. to all the commenters who wrote replies such
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Introduction: Andrew Tanentzap, William Lee, Adrian Monks, Kate Ladley, Peter Johnson, Geoffrey Rogers, Joy Comrie, Dean Clarke, and Ella Hayman write : We tested a multivariate hypothesis about the causal mechanisms underlying plant invasions in an ephemeral wetland in South Island, New Zealand to inform management of this biodiverse but globally imperilled habitat. . . . We found that invasion by non-native plants was lowest in sites where the physical disturbance caused by flooding was both intense and frequent. . . . only species adapted to the dominant disturbance regimes at a site may become successful invaders. Their keywords are: causal networks; community dynamics; functional traits, invasive species, kettlehole; megafauna; rabbits; restoration; turf plants But here’s the part that I like best: We fitted all our models within a hierarchical Bayesian framework using . . . STAN v.1.3 (Stan Development Team 2012) from R v.2.15 (R Development Core Team 2012).
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Introduction: The Stan Development Team is happy to announce CmdStan, RStan, and PyStan v2.2.0. As usual, more info is available on the Stan Home Page . This is a minor release with a mix of bug fixes and features. For a full list of changes, please see the v2.2.0 milestone on stan-dev/stan’s issue tracker. Some of the bug fixes and issues are listed below. Bug Fixes increment_log_prob is now vectorized and compiles with vector arguments multinomial random number generator used the wrong size for the return value fixed memory leaks in auto-diff implementation variables can start with the prefix ‘inf’ fixed parameter output order for arrays when using optimization RStan compatibility issue with latest Rcpp 0.11.0 Features suppress command line output with refresh <= 0 added 1 to treedepth to match usual definition of treedepth added distance, squared_distance, diag_pre_multiply, diag_pre_multiply to Stan modeling lnaguage added a ‘fixed_param’ sampler for
5 0.64555246 1528 andrew gelman stats-2012-10-10-My talk at MIT on Thurs 11 Oct
Introduction: Stan: open-source Bayesian inference Speaker: Andrew Gelman, Columbia University Date: Thursday, October 11 2012 Time: 4:00PM to 5:00PM Location: 32-D507 Host: Polina Golland, CSAIL Contact: Polina Golland, 6172538005, polina@csail.mit.edu Stan ( mc-stan.org ) is an open-source package for obtaining Bayesian inference using the No-U-Turn sampler, a variant of Hamiltonian Monte Carlo. We discuss how Stan works and what it can do, the problems that motivated us to write Stan, current challenges, and areas of planned development, including tools for improved generality and usability, more efficient sampling algorithms, and fuller integration of model building, model checking, and model understanding in Bayesian data analysis. P.S. Here’s the talk .
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Introduction: Andrew Tanentzap, William Lee, Adrian Monks, Kate Ladley, Peter Johnson, Geoffrey Rogers, Joy Comrie, Dean Clarke, and Ella Hayman write : We tested a multivariate hypothesis about the causal mechanisms underlying plant invasions in an ephemeral wetland in South Island, New Zealand to inform management of this biodiverse but globally imperilled habitat. . . . We found that invasion by non-native plants was lowest in sites where the physical disturbance caused by flooding was both intense and frequent. . . . only species adapted to the dominant disturbance regimes at a site may become successful invaders. Their keywords are: causal networks; community dynamics; functional traits, invasive species, kettlehole; megafauna; rabbits; restoration; turf plants But here’s the part that I like best: We fitted all our models within a hierarchical Bayesian framework using . . . STAN v.1.3 (Stan Development Team 2012) from R v.2.15 (R Development Core Team 2012).
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Introduction: Aaron Swartz writes the following, as a lead-in to an argument in favor of vegetarianism: Imagine you were an early settler of what is now the United States. It seems likely you would have killed native Americans. After all, your parents killed them, your siblings killed them, your friends killed them, the leaders of the community killed them, the President killed them. Chances are, you would have killed them too . . . Or if you see nothing wrong with killing native Americans, take the example of slavery. Again, everyone had slaves and probably didn’t think too much about the morality of it. . . . Are these statements true, though? It’s hard for me to believe that most early settlers (from the context, it looks like Swartz is discussing the 1500s-1700s here) killed native Americans. That is, if N is the number of early settlers, and Y is the number of these settlers who killed at least one Indian, I suspect Y/N is much closer to 0 than to 1. Similarly, it’s not even cl
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Introduction: Matthew Yglesias links approvingly to the following statement by Michael Mandel: Homeland Security accounts for roughly 90% of the increase in federal regulatory employment over the past ten years. Roughly 90%, huh? That sounds pretty impressive. But wait a minute . . . what if total federal regulatory employment had increased a bit less. Then Homeland Security could’ve accounted for 105% of the increase, or 500% of the increase, or whatever. The point is the change in total employment is the sum of a bunch of pluses and minuses. It happens that, if you don’t count Homeland Security, the total hasn’t changed much–I’m assuming Mandel’s numbers are correct here–and that could be interesting. The “roughly 90%” figure is misleading because, when written as a percent of the total increase, it’s natural to quickly envision it as a percentage that is bounded by 100%. There is a total increase in regulatory employment that the individual agencies sum to, but some margins are p
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