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Archive of posts tagged R

What does CNN have in common with Carmen Reinhart, Kenneth Rogoff, and Richard Tol: They all made foolish, embarrassing errors that would never have happened had they been using R Markdown

Rachel Cunliffe shares this delight: Had the CNN team used an integrated statistical analysis and display system such as R Markdown, nobody would’ve needed to type in the numbers by hand, and the above embarrassment never would’ve occurred. And CNN should be embarrassed about this: it’s much worse than a simple typo, as it indicates […]

Stan goes to the World Cup

I thought it would be fun to fit a simple model in Stan to estimate the abilities of the teams in the World Cup, then I could post everything here on the blog, the whole story of the analysis from beginning to end, showing the results of spending a couple hours on a data analysis. […]

Comment of the week

This one, from DominikM: Really great, the simple random intercept – random slope mixed model I did yesterday now runs at least an order of magnitude faster after installing RStan 2.3 this morning. You are doing an awesome job, thanks a lot!

(Py, R, Cmd) Stan 2.3 Released

We’re happy to announce RStan, PyStan and CmdStan 2.3. Instructions on how to install at: http://mc-stan.org/ As always, let us know if you’re having problems or have comments or suggestions. We’re hoping to roll out the next release a bit quicker this time, because we have lots of good new features that are almost ready […]

Identifying pathways for managing multiple disturbances to limit plant invasions

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 […]

Bayesian nonparametric weighted sampling inference

Yajuan Si, Natesh Pillai, and I write: It has historically been a challenge to perform Bayesian inference in a design-based survey context. The present paper develops a Bayesian model for sampling inference using inverse-probability weights. We use a hierarchical approach in which we model the distribution of the weights of the nonsampled units in the […]

Transitioning to Stan

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 […]

Stan Model of the Week: PK Calculation of IV and Oral Dosing

[Update: Revised given comments from Wingfeet, Andrew and germo. Thanks! I’d mistakenly translated the dlnorm priors in the first version — amazing what a difference the priors make. I also escaped the less-than and greater-than signs in the constraints in the model so they’re visible. I also updated to match the thin=2 output of JAGS.] […]

Stupid R Tricks: Random Scope

Andrew and I have been discussing how we’re going to define functions in Stan for defining systems of differential equations; see our evolving ode design doc; comments welcome, of course. About Scope I mentioned to Andrew I would prefer pure lexical, static scoping, as found in languages like C++ and Java. If you’re not familiar […]

R package for effect size calculations for psychology researchers

Dan Gerlanc writes: I read your post the other day [now the other month, as our blog is on a bit of a delay] on helping psychologists do research and thought you might be interested in our R package, “bootES”, for robust effect size calculation and confidence interval estimation using resampling techniques. The package provides […]

Scalable Stan

Bob writes: If you have papers that have used Stan, we’d love to hear about it. We finally got some submissions, so we’re going to start a list on the web site for 2.0 in earnest. You can either mail them to the list, to me directly, or just update the issue (at least until […]

Uncertainty in parameter estimates using multilevel models

David Hsu writes:

Priors

Nick Firoozye writes: While I am absolutely sympathetic to the Bayesian agenda I am often troubled by the requirement of having priors. We must have priors on the parameter of an infinite number of model we have never seen before and I find this troubling. There is a similarly troubling problem in economics of utility […]

Please send all comments to /dev/ripley

Trey Causey asks, Has R-help gotten meaner over time?: I began by using Scrapy to download all the e-mails sent to R-help between April 1997 (the earliest available archive) and December 2012. . . . We each read 500 messages and coded them in the following categories: -2 Negative and unhelpful -1 Negative but helpful […]

R sucks

I was trying to make some new graphs using 5-year-old R code and I got all these problems because I was reading in files with variable names such as “co.fipsid” and now R is automatically changing them to “co_fipsid”. Or maybe the names had underbars all along, and the old R had changed them into […]

Job openings at conservative political analytics firm!

After posting that announcement about Civis Analytics, I wrote, “If a reconstituted Romney Analytics team is hiring, let me know and I’ll post that ad too.” Adam Schaeffer obliged: Not sure about Romney’s team, but Evolving Strategies is looking for sharp folks who lean right: Evolving Strategies is a political communications research firm specializing in […]

Job opening! Come work with us!

Postdoctoral position in statistical modeling of social networks A full-time postdoctoral position is available beginning Fall 2014 in the research group of Tian Zheng and Andrew Gelman working on statistical analysis and modeling of social network data, in close cooperation with our experimental collaborators. Four key papers of this project so far are: http://www.stat.columbia.edu/~gelman/research/published/overdisp_final.pdf http://nersp.osg.ufl.edu/~ufruss/documents/mccormick_salganik_zheng10.pdf […]

Robust logistic regression

Corey Yanofsky writes: In your work, you’ve robustificated logistic regression by having the logit function saturate at, e.g., 0.01 and 0.99, instead of 0 and 1. Do you have any thoughts on a sensible setting for the saturation values? My intuition suggests that it has something to do with proportion of outliers expected in the […]

Stan!

Guy Freeman writes: I thought you’d all like to know that Stan was used and referenced in a peer-reviewed Rapid Communications paper on influenza. Thank you for this excellent modelling language and sampler, which made it possible to carry out this work quickly! I haven’t actually read the paper, but I’m happy to see Stan […]

Stan 1.3.0 and RStan 1.3.0 Ready for Action

The Stan Development Team is happy to announce that Stan 1.3.0 and RStan 1.3.0 are available for download. Follow the links on: Stan home page: http://mc-stan.org/ Please let us know if you have problems updating. Here’s the full set of release notes. v1.3.0 (12 April 2013) ====================================================================== Enhancements ———————————- Modeling Language * forward sampling (random […]