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Reproducibility and Replicability in Science (2019)
THE NATIONAL ACADEMIES PRESS This PDF is available at http://nap.edu/25303 SHARE Reproducibility and Replicability in Science (2019) DETAILS 256 pages | 6 x 9 | PAPERBACK ISBN 978-0-309-48616-3 | DOI 10.17226/25303 CONTRIBUTORS GET THIS BOOK Committee on Reproducibility and Replicability in Science; Board on Behavioral, Cognitive, and Sensory Sciences; Committee on National Statistics; Division of Behavioral and Social Sciences and Education; Nuclear and Radiation Studies FIND RELATED TITLES Board; Division on Earth and Life Studies; Board on Mathematical Sciences and Analytics; Committee on Applied and Theoretical Statistics; Division on SUGGESTEDEngineering an CITATIONd Physical Sciences; Board on Research Data and Information; NCaotmiomnaittl eAec oand eSmciiesn coef ,S Ecniegninceeesr, inEgn,g Mineedeircininge,, aanndd MPeudbilcicin Peo 2li0c1y;9 P. Roleicpyr oadnudcibility aGnlodb Rael Aplfifcaairbsi;li tNy ainti oSncaiel Ancea.d Wemaisehsi nogf tSonc,ie DnCce: sT,h Een Ngianteioenrainl gA, caandde Mmeiedsi cPinress. https://doi.org/10.17226/25303. Visit the National Academies Press at NAP.edu and login or register to get: – Access to free PDF downloads of thousands of scientific reports – 10% off the price of print titles – Email or social media notifications of new titles related to your interests – Special offers and discounts Distribution, posting, or copying of this PDF is strictly prohibited without written permission of the National Academies Press. (Request Permission) Unless otherwise indicated, all materials in this -
The R and Omegahat Projects in Statistical Computing
The R and Omegahat Projects in Statistical Computing Brian D. Ripley [email protected] http://www.stats.ox.ac.uk/∼ripley Outline • Statistical Computing – History – S – R • Application and Comparisons – Web servers – Embedding — Medieval Chant – R vs S-PLUS • Omegahat and Component Systems – The Omegahat project – Components — GGobi – The future? Statistical Computing and S Scene-setting: Statistical Computing 1980 Mainly Fortran programming, or PL/I (SAS). Batch computing (SAS, BMDP, SPSS, Genstat) with restricted range of platforms. Some small interactive systems (GLIM 3.77, Minitab). Very poor interactive graphics (2400 baud to a Tektronix storage tube if you were lucky). Flatbed and drum plotters, microfilm for publication-quality output off-line. Mainly home-brew solutions in research. (GLIM macros?) 1990 PCs become widespread, but FPUs still uncommon. Sun etc workstations available for researchers, and for teaching in a few places. Graphics could be pretty good (postscript printers, ca 1000 × 1000 pixel screens), but often was not, and mono text terminals were still widespread. C was beginning to be used, as more portable than Fortran. (Few PC Fortran compilers then and now.) Still SAS, SPSS etc as batch programs. S beginning to be make an impact on research and teaching. 2001 Little spread in machine speed (min 500MHz, max 1.5GHz), fast FPUs are universal. Colour everywhere, usually 24-bit colour. The video-games generation is now at university. Few people would dream of writing a complete program for a research idea: prototype and distribute in a higher-level language such as S or Matlab or Gauss or Ox or ... -
Hadley Wickham, the Man Who Revolutionized R Hadley Wickham, the Man Who Revolutionized R · 51,321 Views · More Stats
12/15/2017 Hadley Wickham, the Man Who Revolutionized R Hadley Wickham, the Man Who Revolutionized R · 51,321 views · More stats Share https://priceonomics.com/hadley-wickham-the-man-who-revolutionized-r/ 1/10 12/15/2017 Hadley Wickham, the Man Who Revolutionized R “Fundamentally learning about the world through data is really, really cool.” ~ Hadley Wickham, prolific R developer *** If you don’t spend much of your time coding in the open-source statistical programming language R, his name is likely not familiar to you -- but the statistician Hadley Wickham is, in his own words, “nerd famous.” The kind of famous where people at statistics conferences line up for selfies, ask him for autographs, and are generally in awe of him. “It’s utterly utterly bizarre,” he admits. “To be famous for writing R programs? It’s just crazy.” Wickham earned his renown as the preeminent developer of packages for R, a programming language developed for data analysis. Packages are programming tools that simplify the code necessary to complete common tasks such as aggregating and plotting data. He has helped millions of people become more efficient at their jobs -- something for which they are often grateful, and sometimes rapturous. The packages he has developed are used by tech behemoths like Google, Facebook and Twitter, journalism heavyweights like the New York Times and FiveThirtyEight, and government agencies like the Food and Drug Administration (FDA) and Drug Enforcement Administration (DEA). Truly, he is a giant among data nerds. *** Born in Hamilton, New Zealand, statistics is the Wickham family business: His father, Brian Wickham, did his PhD in the statistics heavy discipline of Animal Breeding at Cornell University and his sister has a PhD in Statistics from UC Berkeley. -
Sources of False Positives and False Negatives in the STATCHECK 1 Algorithm: Reply to Nuijten Et Al
Cite as: Schmidt, T. (2017). Sources of false positives and false negatives in the STATCHECK 1 algorithm: Reply to Nuijten et al. (2016). arXiv:1610.01010v8 [q-bio.NC] Sources of false positives and false negatives in the STATCHECK algorithm: Reply to Nuijten et al. (2016) arXiv:1610.01010v8 [q-bio.NC] Thomas Schmidt University of Kaiserslautern, Germany Experimental Psychology Unit www.sowi.uni-kl.de/psychologie [email protected] Abstract STATCHECK is an R algorithm designed to scan papers automatically for inconsistencies between test statistics and their associated p values (Nuijten et al., 2016). The goal of this comment is to point out an important and well-documented flaw in this busily applied algorithm: It cannot handle corrected p values. As a result, statistical tests applying appropriate corrections to the p value (e.g., for multiple tests, post-hoc tests, violations of assumptions, etc.) are likely to be flagged as reporting inconsistent statistics, whereas papers omitting necessary corrections are certified as correct. The STATCHECK algorithm is thus valid for only a subset of scientific papers, and conclusions about the quality or integrity of statistical reports should never be based solely on this program. STATCHECK is an R algorithm designed to scan papers automatically for inconsistencies between test statistics and their reported p values (Nuijten, Hartgerink, van Assen, Epskamp, and Wicherts, 2016). The algorithm is currently part of these authors’ project to automatically scan thousands of scientific psychology papers and post the results on a website when the program detects some discrepancy, essentially flagging papers for reporting inconsistent test statistics. -
R Generation [1] 25
IN DETAIL > y <- 25 > y R generation [1] 25 14 SIGNIFICANCE August 2018 The story of a statistical programming they shared an interest in what Ihaka calls “playing academic fun language that became a subcultural and games” with statistical computing languages. phenomenon. By Nick Thieme Each had questions about programming languages they wanted to answer. In particular, both Ihaka and Gentleman shared a common knowledge of the language called eyond the age of 5, very few people would profess “Scheme”, and both found the language useful in a variety to have a favourite letter. But if you have ever been of ways. Scheme, however, was unwieldy to type and lacked to a statistics or data science conference, you may desired functionality. Again, convenience brought good have seen more than a few grown adults wearing fortune. Each was familiar with another language, called “S”, Bbadges or stickers with the phrase “I love R!”. and S provided the kind of syntax they wanted. With no blend To these proud badge-wearers, R is much more than the of the two languages commercially available, Gentleman eighteenth letter of the modern English alphabet. The R suggested building something themselves. they love is a programming language that provides a robust Around that time, the University of Auckland needed environment for tabulating, analysing and visualising data, one a programming language to use in its undergraduate statistics powered by a community of millions of users collaborating courses as the school’s current tool had reached the end of its in ways large and small to make statistical computing more useful life. -
Statistics with Free and Open-Source Software
Free and Open-Source Software • the four essential freedoms according to the FSF: • to run the program as you wish, for any purpose • to study how the program works, and change it so it does Statistics with Free and your computing as you wish Open-Source Software • to redistribute copies so you can help your neighbor • to distribute copies of your modified versions to others • access to the source code is a precondition for this Wolfgang Viechtbauer • think of ‘free’ as in ‘free speech’, not as in ‘free beer’ Maastricht University http://www.wvbauer.com • maybe the better term is: ‘libre’ 1 2 General Purpose Statistical Software Popularity of Statistical Software • proprietary (the big ones): SPSS, SAS/JMP, • difficult to define/measure (job ads, articles, Stata, Statistica, Minitab, MATLAB, Excel, … books, blogs/posts, surveys, forum activity, …) • FOSS (a selection): R, Python (NumPy/SciPy, • maybe the most comprehensive comparison: statsmodels, pandas, …), PSPP, SOFA, Octave, http://r4stats.com/articles/popularity/ LibreOffice Calc, Julia, … • for programming languages in general: TIOBE Index, PYPL, GitHut, Language Popularity Index, RedMonk Rankings, IEEE Spectrum, … • note that users of certain software may be are heavily biased in their opinion 3 4 5 6 1 7 8 What is R? History of S and R • R is a system for data manipulation, statistical • … it began May 5, 1976 at: and numerical analysis, and graphical display • simply put: a statistical programming language • freely available under the GNU General Public License (GPL) → open-source -
A History of R (In 15 Minutes… and Mostly in Pictures)
A history of R (in 15 minutes… and mostly in pictures) JULY 23, 2020 Andrew Zief!ler Lunch & Learn Department of Educational Psychology RMCC University of Minnesota LATIS Who am I and Some Caveats Andy Zie!ler • I teach statistics courses in the Department of Educational Psychology • I have been using R since 2005, when I couldn’t put Me (on the EPSY faculty board) SAS on my computer (it didn’t run natively on a Me Mac), and even if I could have gotten it to run, I (everywhere else) couldn’t afford it. Some caveats • Although I was alive during much of the era I will be talking about, I was not working as a statistician at that time (not even as an elementary student for some of it). • My knowledge is second-hand, from other people and sources. Statistical Computing in the 1970s Bell Labs In 1976, scientists from the Statistics Research Group were actively discussing how to design a language for statistical computing that allowed interactive access to routines in their FORTRAN library. John Chambers John Tukey Home to Statistics Research Group Rick Becker Jean Mc Rae Judy Schilling Doug Dunn Introducing…`S` An Interactive Language for Data Analysis and Graphics Chambers sketch of the interface made on May 5, 1976. The GE-635, a 36-bit system that ran at a 0.5MIPS, starting at $2M in 1964 dollars or leasing at $45K/month. ’S’ was introduced to Bell Labs in November, but at the time it did not actually have a name. The Impact of UNIX on ’S' Tom London Ken Thompson and Dennis Ritchie, creators of John Reiser the UNIX operating system at a PDP-11. -
Changes on CRAN 2014-07-01 to 2014-12-31
NEWS AND NOTES 192 Changes on CRAN 2014-07-01 to 2014-12-31 by Kurt Hornik and Achim Zeileis New packages in CRAN task views Bayesian BayesTree. Cluster fclust, funFEM, funHDDC, pgmm, tclust. Distributions FatTailsR, RTDE, STAR, predfinitepop, statmod. Econometrics LinRegInteractive, MSBVAR, nonnest2, phtt. Environmetrics siplab. Finance GCPM, MSBVAR, OptionPricing, financial, fractal, riskSimul. HighPerformanceComputing GUIProfiler, PGICA, aprof. MachineLearning BayesTree, LogicForest, hdi, mlr, randomForestSRC, stabs, vcrpart. MetaAnalysis MAVIS, ecoreg, ipdmeta, metaplus. NumericalMathematics RootsExtremaInflections, Rserve, SimplicialCubature, fastGHQuad, optR. OfficialStatistics CoImp, RecordLinkage, rworldmap, tmap, vardpoor. Optimization RCEIM, blowtorch, globalOptTests, irace, isotone, lbfgs. Phylogenetics BAMMtools, BoSSA, DiscML, HyPhy, MPSEM, OutbreakTools, PBD, PCPS, PHYLOGR, RADami, RNeXML, Reol, Rphylip, adhoc, betapart, dendextend, ex- pands, expoTree, jaatha, kdetrees, mvMORPH, outbreaker, pastis, pegas, phyloTop, phyloland, rdryad, rphast, strap, surface, taxize. Psychometrics IRTShiny, PP, WrightMap, mirtCAT, pairwise. ReproducibleResearch NMOF. Robust TEEReg, WRS2, robeth, robustDA, robustgam, robustloggamma, robustreg, ror, rorutadis. Spatial PReMiuM. SpatioTemporal BayesianAnimalTracker, TrackReconstruction, fishmove, mkde, wildlifeDI. Survival DStree, ICsurv, IDPSurvival, MIICD, MST, MicSim, PHeval, PReMiuM, aft- gee, bshazard, bujar, coxinterval, gamboostMSM, imputeYn, invGauss, lsmeans, multipleNCC, paf, penMSM, spBayesSurv, -
The Grammar of Graphics: an Introduction to Ggplot2
The Grammar of Graphics: An Introduction to ggplot2 Jefferson Davis Research Analytics Historical background • In 1970s John Chambers, Rick Becker, and Allan Wilks develop S and S+ at Bell Labs • Bell System monopoly was broken up in 1982 • Late 80s some attempt to commercialize S/S+ but already too many non-commercial implementations • Ross Ihaka and Robert Gentleman produce R in early 1990s • Main developement now by Comprehensive R Archive Network (CRAN) R: some useful tidbits • Use left arrow for variable assignment x <- 5 • Can concatenate with c() c(4,5) [1] 4 5 c("baby","elephant”) [1] "baby" "elephant" • Download packages with the install() command install.packages("ggplot2") install.packages("ggthemes") • Make packages available with library() library(ggplot2) library(ggthemes) • Get help with ? ? ggplot Statistical Graphics x y=x2 0.0 0.00 0.1 0.01 0.2 0.04 0.3 0.09 0.4 0.16 0.5 0.25 0.6 0.36 0.7 0.49 0.8 0.64 0.9 0.81 1.0 1.00 1.1 1.21 1.2 1.44 1.3 1.69 Statistical Graphics Graphics can convey meaning without displaying any particular quantatative data. Statistical Graphics Statistical Graphics The dataset The diamond dataset carat cut color clarity depth table price x y z 0.23 Ideal E SI2 61.5 55 326 3.95 3.98 2.43 0.21 Premium E SI1 59.8 61 326 3.89 3.84 2.31 0.23 Good E VS1 56.9 65 327 4.05 4.07 2.31 0.29 Premium I VS2 62.4 58 334 4.2 4.23 2.63 0.31 Good J SI2 63.3 58 335 4.34 4.35 2.75 Very 0.24 Good J VVS2 62.8 57 336 3.94 3.96 2.48 The dataset The dataset library(ggplot2) head(diamonds)[,1:4] # A tibble: 6 × 4 carat cut -
Data Analysis in R
Data analysis in R IML Machine Learning Workshop, CERN Andrew John Lowe Wigner Research Centre for Physics, Hungarian Academy of Sciences Downloading and installing R You can follow this tutorial while I talk 1. Download R for Linux, Mac or Windows from the Comprehensive R Archive Network (CRAN): https://cran.r-project.org/ I Latest release version 3.3.3 (2017-03-06, “Another Canoe”) RStudio is a free and open-source integrated development environment (IDE) for R 2. Download RStudio from https://www.rstudio.com/ I Make sure you have installed R first! I Get RStudio Desktop (free) I Latest release version 1.0.136 Rstudio RStudio looks something like this. RStudio RStudio with source editor, console, environment and plot pane. What is R? I An open source programming language for statistical computing I R is a dialect of the S language I S is a language that was developed by John Chambers and others at Bell Labs I S was initiated in 1976 as an internal statistical analysis environment — originally implemented as FORTRAN libraries I Rewritten in C in 1988 I S continues to this day as part of the GNU free software project I R created by Ross Ihaka and Robert Gentleman in 1991 I First announcement of R to the public in 1993 I GNU General Public License makes R free software in 1995 I Version 1.0.0 release in 2000 Why use R? I Free I Runs on almost any standard computing platform/OS I Frequent releases; active development I Very active and vibrant user community I Estimated ~2 million users worldwide I R-help and R-devel mailing lists, Stack Overflow I Frequent conferences; useR!, EARL, etc. -
The Reproducibility of Statistical Results in Psychological Research: an Investigation Using Unpublished Raw Data
Psychological Methods © 2020 American Psychological Association 2020, Vol. 2, No. 999, 000 ISSN: 1082-989X https://doi.org/10.1037/met0000365 The Reproducibility of Statistical Results in Psychological Research: An Investigation Using Unpublished Raw Data Richard Artner, Thomas Verliefde, Sara Steegen, Sara Gomes, Frits Traets, Francis Tuerlinckx, and Wolf Vanpaemel Faculty of Psychology and Educational Sciences, KU Leuven Abstract We investigated the reproducibility of the major statistical conclusions drawn in 46 articles published in 2012 in three APA journals. After having identified 232 key statistical claims, we tried to reproduce, for each claim, the test statistic, its degrees of freedom, and the corresponding p value, starting from the raw data that were provided by the authors and closely following the Method section in the article. Out of the 232 claims, we were able to successfully reproduce 163 (70%), 18 of which only by deviating from the article’s analytical description. Thirteen (7%) of the 185 claims deemed significant by the authors are no longer so. The reproduction successes were often the result of cumbersome and time-consuming trial-and-error work, suggesting that APA style reporting in conjunction with raw data makes numerical verification at least hard, if not impossible. This article discusses the types of mistakes we could identify and the tediousness of our reproduction efforts in the light of a newly developed taxonomy for reproducibility. We then link our findings with other findings of empirical research on this topic, give practical recommendations on how to achieve reproducibility, and discuss the challenges of large-scale reproducibility checks as well as promising ideas that could considerably increase the reproducibility of psychological research. -
Ross Ihaka and Robert Gentleman Year
MULTI-FILES LOADER file_01.csv GGPLOT2 data(aes) + geom + labels R (Statistical computing) files <- list.files(pattern = “file_.*csv”) %>% ggplot(data = df, aes(x_col, y_col)) + geom() CHEET SHEET BY @CHARLSTOWN lapply(files, read_csv) %>% canvas + layer +...+ labs > general structure CREATOR: ROSS IHAKA AND ROBERT GENTLEMAN bind_rows() YEAR: 1995 ggplot(data = df) > canvas level STEABLE RELEASE (2020): 3.6.2 DATA FRAMES ACTIONS dplyr & tidyr aes(x_col, y_col) > aesthetics TOP FROM R: DPLYR, GGPLOT2, ESQUISSE, BIO DPLYR FUNCTIONS df %>% function geom_point() > geometry level CONDUCTOR, SHINY, LUBRIDATE, KNITR, TIDYR. rename(c_new = c1) > rename columns AESTHETICS aes(*) select(col) > select column ELEMENTAL LIBRARIES library( * ) select(-col) > drop column x_col, y_col > sets x,y elements library(dplyr) > Easy commands filter(c == “value”) > select rows label=labels > set ticks labels library(ggplot2) > Generate plots mutate(col = (c1+2)) > column mutations color > sets color values library(tidyverse) > Tidy data transmute(c1=c, c2=c/2) > drop & built cols alpha > sets the opacity library(kableExtra) > Table visualization arrange(des(c1)) > order by column fill = ‘color’ > sets the filled color library(knitr) > Regular exprs. join(df2) > join 2 data frames shape = class > set groups by class library(readr) > Files Reader group_by(col) > silent grooping group = class > sets plot by class library(stringr) > Strings treatment summarise(c* = max(c)) > after grooping shape > geometry shape size = 4 > sets the size TIDYR FUNCTIONS df %>% function