http://blogs.uit.tufts.edu/statisticalcomputingmatters/ Suggestions and comments about obscure and useful software. en Copyright 2009 Fri, 20 Nov 2009 14:30:12 -0500 http://www.sixapart.com/movabletype/ http://blogs.law.harvard.edu/tech/rss ICPSR. Over the years, a typical use case would be for a researcher to download data for local analysis. In this way one would have maximal access to raw data and use of analysis tools of one's choice. Recently, ICPSR has provided a web front end tool, Survey Documentation Analysis (SDA), for online analysis suitable for routine statistical queries. Many datasets in the archive have been prepared for use with this tool. SDA is available to any Tufts faculty, staff or students. It would seem that use of this interface in support of some class teaching activities would lower the barrier to analysis that a more traditional approach would require.]]> http://blogs.uit.tufts.edu/statisticalcomputingmatters/#007414 http://blogs.uit.tufts.edu/statisticalcomputingmatters/#007414 Fri, 20 Nov 2009 14:30:12 -0500 article caught my attention that solves in a unified manner a class of gene array analysis problems using the work horse methodology of statistical Mixed Models. The approach taken is presented for a two group time course experiment. Extension to more complex experimental designs is discussed. A comparison is made to several competing approaches, including a simulation study, and the featured method is implemented in SAS software. However, any statistical package supporting Spectral Decomposition and Mixed Models may be used. ]]> http://blogs.uit.tufts.edu/statisticalcomputingmatters/#007328 http://blogs.uit.tufts.edu/statisticalcomputingmatters/#007328 Tue, 10 Nov 2009 14:28:13 -0500 Epi has been around for a long time, starting back in the days of DOS! Over the years Epi has matured into a suite of tools(Pepi) that has the fields of public health and epidemiology as its focus. WINPEPI software for the Windows platform is free and offers a board mix of software that general statistics users might consider as an alternative to more costly options. Epi is like the Energizer Bunny, it is the gift that keeps going and going....]]> http://blogs.uit.tufts.edu/statisticalcomputingmatters/#007293 http://blogs.uit.tufts.edu/statisticalcomputingmatters/#007293 Fri, 06 Nov 2009 11:44:55 -0500 Andrew Gleman on the topic presents an overview, simulation results and examples demonstrating the benefits in an applied setting. Suggestions on the use of R and other software is mentioned for implementation. ]]> http://blogs.uit.tufts.edu/statisticalcomputingmatters/#007254 http://blogs.uit.tufts.edu/statisticalcomputingmatters/#007254 Fri, 30 Oct 2009 14:32:45 -0500 JASA Sept. 2009 article, Weighted Normal Spatial Scan Statistic for Heterogeneous Population Data, the authors offer a solution that addresses this problem in more generality. Weights related to local variance measures or proxies such as sample size can be created for use in a weighted likelihood approach. Extensions to non gaussian probability models are addressed. Some case studies and power simulations provided suggest excellent performance. Their solution has been implemented in the freely available software Satscan.]]> http://blogs.uit.tufts.edu/statisticalcomputingmatters/#007232 http://blogs.uit.tufts.edu/statisticalcomputingmatters/#007232 Tue, 27 Oct 2009 11:07:07 -0500 mixAK is a new R package for Bayesian estimation of multivariate normal mixtures allowing for selection of the number of mixture components, density estimation and optionally allows for interval-censored multivariate data. Author Arnost Komarek's journal article Computational Statistics and Data Analysis, Volume 53, Issue 12, October 2009, presents the underlying theory and application of the new approach using RJ-MCMC estimation. The selection of the number of mixture components is aided by Deviance Information Criterion(DIC) and Penalized Expected Deviance(PED) measures. ]]> http://blogs.uit.tufts.edu/statisticalcomputingmatters/#007019 http://blogs.uit.tufts.edu/statisticalcomputingmatters/#007019 Tue, 06 Oct 2009 15:15:55 -0500 ANES datasets can be found in the Sept. 2009 Technical Report, nes012427, Computing Weights for American National Election Study Survey Data. The report can be found in the Reference Library section of the ANES website. Single panel cross-sectional, two-wave panel and multi-wave panel recommendations are considered along with nonresponse and poststratification weighting. The generality of discussion applies to other large studies such as Census data, and similar surveys. ]]> http://blogs.uit.tufts.edu/statisticalcomputingmatters/#006806 http://blogs.uit.tufts.edu/statisticalcomputingmatters/#006806 Thu, 17 Sep 2009 14:47:09 -0500 JCGS. In short, the authors cleverly reparameterized and recast the problem so as to allow efficient MCMC samplers to address the Bayesian estimation task. Their article's supplemental materials provide the R and OpenBugs codes to address the efficient estimation tasks outlined. ]]> http://blogs.uit.tufts.edu/statisticalcomputingmatters/#006762 http://blogs.uit.tufts.edu/statisticalcomputingmatters/#006762 Wed, 09 Sep 2009 11:29:22 -0500 Spss software has an extensive tutorial built into its product and most first time users will benefit from using it. Additional Spss resources can be found here. ]]> http://blogs.uit.tufts.edu/statisticalcomputingmatters/#006755 http://blogs.uit.tufts.edu/statisticalcomputingmatters/#006755 Tue, 08 Sep 2009 15:49:10 -0500 R software. Now that the fall semester is upon us, we have added many new R BioInformatic packages to the baseline R installation on our research linux cluster. This option provides a scalable solution to those needing additional computing power. ]]> http://blogs.uit.tufts.edu/statisticalcomputingmatters/#006741 http://blogs.uit.tufts.edu/statisticalcomputingmatters/#006741 Thu, 03 Sep 2009 15:00:54 -0500 Winbugs came along and offered a higher level interface, similar to what Matlab did for linear algebra syntax and functionality, but closer in spirit to the notation used by Statisticans to depict multilevel probability based models. While all of these still have their pros and cons, we find now an explosion of Bayesian solutions implemented in R with the benefit of object orientation. If one takes a look at the "CRAN Task View: Bayesian Inference" page on the R site maintained by Jong Hee Park, one will find 60+ packages with numerous solutions to many standard statistical modeling problems. Of the many listed, note the package BAS for Bayesian Model Averaging in linear models using stochastic or deterministic sampling without replacement from posterior distributions. Prior distributions on coefficients are from Zellner's g-prior or mixtures of g-priors corresponding to the Zellner-Siow Cauchy Priors or the Liang et al hyper-g priors. The stochastic search capability allows for model specification searches that would not have been possible a few years ago with the ease that is now possible. ]]> http://blogs.uit.tufts.edu/statisticalcomputingmatters/#006504 http://blogs.uit.tufts.edu/statisticalcomputingmatters/#006504 Tue, 14 Jul 2009 11:51:59 -0500 InferenceforR. This product allow for the use of R within Excel. See the following screencast for a slick presentation.]]> http://blogs.uit.tufts.edu/statisticalcomputingmatters/#006301 http://blogs.uit.tufts.edu/statisticalcomputingmatters/#006301 Thu, 21 May 2009 15:26:35 -0500 javanumerics. A large variety of statistical and mathematical classes are available. Note, not all options are free. ]]> http://blogs.uit.tufts.edu/statisticalcomputingmatters/#006250 http://blogs.uit.tufts.edu/statisticalcomputingmatters/#006250 Wed, 06 May 2009 12:04:56 -0500 SAS IML, GAUSS, Stata, Splus and R. Yet others will seek stand alone solutions in one form or another. These range from public domain C, C++, Fortran, and Java research subrountines to stand-alone programs with various user interfaces. The goal of this blog is to list references and short descriptions of various solutions that may offer additional insights into your research and the statistical methods, and maybe even save you some time. About a dozen or so topics some to mind and I hope to address them shortly. These posts are not intended as statistical guidance nor endorsement. Most problems are best addressed by the advice of an experienced practitioner in the relevant field.]]> http://blogs.uit.tufts.edu/statisticalcomputingmatters/#006191 http://blogs.uit.tufts.edu/statisticalcomputingmatters/#006191 Wed, 29 Apr 2009 10:55:08 -0500 PiFace as a power calculation tool. However SAS users may find the following three SAS macros of interest. UnifyPow is an extensive collection of power calculators implemented in SAS as a Macro. A SAS proceedings paper about UnifyPow discusses its broad generality. The second macro is rpower and addresses the reprospective aspect of the issue. The third macro, glimmixsamplesize, is designed to use the generality of SAS's Proc Glimmix for generalized linear mixed models. These macros provide a substantial increase in the number of settings that can be addressed for power calculations. ]]> http://blogs.uit.tufts.edu/statisticalcomputingmatters/#006020 http://blogs.uit.tufts.edu/statisticalcomputingmatters/#006020 Mon, 13 Apr 2009 14:20:25 -0500