andrew_gelman_stats andrew_gelman_stats-2010 andrew_gelman_stats-2010-41 knowledge-graph by maker-knowledge-mining
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Introduction: Patricia and I have cleaned up some of the R and Bugs code and collected the data for almost all the examples in ARM. See here for links to zip files with the code and data.
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2 See here for links to zip files with the code and data. [sent-2, score-1.358]
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same-blog 1 1.0 41 andrew gelman stats-2010-05-19-Updated R code and data for ARM
Introduction: Patricia and I have cleaned up some of the R and Bugs code and collected the data for almost all the examples in ARM. See here for links to zip files with the code and data.
2 0.19164307 42 andrew gelman stats-2010-05-19-Updated solutions to Bayesian Data Analysis homeworks
Introduction: Here are solutions to about 50 of the exercises from Bayesian Data Analysis. The solutions themselves haven’t been updated; I just cleaned up the file: some change in Latex had resulted in much of the computer code running off the page, so I went in and cleaned up the files. I wrote most of these in 1996, and I like them a lot. I think several of them would’ve made good journal articles, and in retrospect I wish I’d published them as such. Original material that appears first in a book (or, even worse, in homework solutions) can easily be overlooked.
Introduction: David Karger writes: Your recent post on sharing data was of great interest to me, as my own research in computer science asks how to incentivize and lower barriers to data sharing. I was particularly curious about your highlighting of effort as the major dis-incentive to sharing. I would love to hear more, as this question of effort is on we specifically target in our development of tools for data authoring and publishing. As a straw man, let me point out that sharing data technically requires no more than posting an excel spreadsheet online. And that you likely already produced that spreadsheet during your own analytic work. So, in what way does such low-tech publishing fail to meet your data sharing objectives? Our own hypothesis has been that the effort is really quite low, with the problem being a lack of *immediate/tangible* benefits (as opposed to the long-term values you accurately describe). To attack this problem, we’re developing tools (and, since it appear
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Introduction: Daniel Corsi writes: I was wondering if you could help me with some code to set up a posterior predictive check for an unordered multinomial multilevel model. In this case the outcome is categories of bmi (underweight, nomral weight, and overweight) based on individuals from 360 different areas. What I would like to do is set up a replicated dataset to see how the number of overweight/underweight/normal weight individuals based on the model compares to the actual data and some kind of a graphical summary. I am following along with chapter 24 of the arm book but I want to verify that the replicated data accounts for the multilevel structure of the data of people within areas. I am attaching the code I used to run a simple model with only 2 predictors (area wealth and urban/rural designation). My reply: The Bugs code is a bit much for me to look at–but I do recommend that you run it from R, which will give you more flexibility in preprocessing and postprocessing the data. Beyon
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Introduction: Patricia and I have cleaned up some of the R and Bugs code and collected the data for almost all the examples in ARM. See here for links to zip files with the code and data.
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Introduction: Peter Goff wrote: I’m using your text, Data Analysis Using Regression & Multilevel/Hierarchical Models as the basis for an independent study class this fall. I am fairly adapt with Stata, however I have no expertise in R (changing this condition is a goal of the independent study!). I’m working to get up and running with the examples from the book, but I’m running into several problems, all apparently stemming from my having a Mac as opposed to a PC. Specifically I cannot load the “arm” library because I cannot install the lme4 library as lme4 is not available for Macs. Yu-Sung replied: Here are steps for you to install lme4: 1. update your x11 code for Mac system (so that you have gcc and g77 complier) 2. download source code for lme4 from the CRAN. 3. install lme4 from the source you just downloaded. I am not a Mac user. I am adapting steps from installing lme4 in a linux OS. But we have colleagues here following the same instructions and make lme4 working on
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Introduction: Factual collects data on a variety of topics, organizes them, and allows easy access. If you ever wanted to do a histogram of calorie content in Starbucks coffees or plot warnings with a live feed of earthquake data – your life should be a bit simpler now. Also see DataMarket , InfoChimps , and a few older links in The Future of Data Analysis . If you access the data through the API, you can build live visualizations like this: Of course, you could just go to the source. Roy Mendelssohn writes (with minor edits): Since you are both interested in data access, please look at our service ERDDAP: http://coastwatch.pfel.noaa.gov/erddap/index.html http://upwell.pfeg.noaa.gov/erddap/index.html Please do not be fooled by the web pages. Everything is a service (including search and graphics) and the URL completely defines the request, and response formats are easily changed just by changing the “file extension”. The web pages are just html and javascript that u
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Introduction: Patricia and I have cleaned up some of the R and Bugs code and collected the data for almost all the examples in ARM. See here for links to zip files with the code and data.
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