DOCSLIB.ORG

The Regress Function

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
The Regress Function News The Newsletter of the R Project Volume 6/2, May 2006 Editorial by Paul Murrell signing clinical trials. Using Sweave in clinical prac- tice is the topic of Sven Garbade and Peter Burgard’s Welcome to the first regular issue of R News for 2006. article, while M. Wangler, J. Beyersmann, and M. This is a bumper issue, with a thirteen contributed Schumacher focus on length of hospital stay with the articles. Many thanks to the contributors and review- changeLOS package. ers for all of the enthusiasm and effort that they put Next, there are three graphics articles. Nitin Jain into these articles. and Gregory Warnes describe the “balloonplot”, a This issue begins with some “methodological” tool for visualizing tabulated data. Jung Zhao ex- papers. Peter Ruckdeschel, Matthias Kohl, Thomas plains how to produce pedigree plots in R, and Brian Stabla, and Florian Camphausen introduce their Ripley and I describe some improvements in R’s font distr package, which provides a set of S4 classes and support for PDF and PostScript graphics output. methods for defining distributions of random vari- SAS users struggling to make the transition to R ables. This is followed by an article from David Clif- might enjoy Søren Højsgaard’s article on his doBy ford and Peter McCullagh on the many uses of the package and the contributed articles are rounded out regress package, then Lukasz Komsta describes his by two pieces from Robin Hankin: one on the onion outliers package, which contains general tests for package for normed division algebras and one on outlying observations. the ResistorArray package for analysing resistor net- The next set of articles involve applications of R works. to particular settings. David Kane and Jeff Enos de- David Meyer provides us with a review of the scribe their portfolio package for working with eq- book “R Graphics”. uity portfolios. Roman Pahl, Andreas Ziegler, and This issue accompanies the release of R version Inke König discuss the GroupSeq package for de- 2.3.0. Changes in R itself and new CRAN packages Contents of this issue: Non-Standard Fonts in PostScript and PDF Graphics . 41 Editorial . 1 The doBy package . 47 S4 Classes for Distributions . 2 Normed division algebras with R: introducing The regress function . 6 the onion package . 49 Processing data for outliers . 10 Resistor networks R: Introducing the Resis- Analysing equity portfolios in R . 13 torArray package . 52 GroupSeq—Designing clinical trials using The good, the bad, and the ugly—Review of group sequential designs . 21 Paul Murrell’s new book: “R Graphics” . 54 Using R/Sweave in everyday clinical practice . 26 Changes in R . 54 changeLOS: An R-package for change in Changes on CRAN . 64 length of hospital stay based on the Aalen- R Foundation News . 70 Johansen estimator . 31 News from the Bioconductor Project . 70 Balloon Plot . 35 Forthcoming Events: DSC 2007 . 71 Drawing pedigree diagrams with R and graphviz . 38 Vol. 6/2, May 22, 2006 2 that have appeared since the release of R 2.2.0 are de- conductor packages. scribed in the relevant regular sections. There is also Finally, I would like to remind everyone of the an update of individuals and institutions who are upcoming useR! 2006 conference. It has attracted providing support for the R Project via the R Foun- a huge number of presenters and promises to be a dation and there is an announcement and call for great success. I hope to see you there! abstracts for the DSC 2007 conference to be held in Auckland, New Zealand. A new addition with this issue is “News from Paul Murrell the Bioconductor Project” by Seth Falcon, which de- The University of Auckland, New Zealand scribes important features of the 1.8 release of Bio- [email protected] S4 Classes for Distributions by Peter Ruckdeschel, Matthias Kohl, Thomas Stabla, and dispatching mechanism decide what to do at run- Florian Camphausen time. In particular, we would like to automatically generate the corresponding functions r, d, p, and q distr is an R package that provides a conceptual treat- for the law of expressions like X+3Y for objects X and Y ment of random variables (r.v.’s) by means of S4– of class Distribution, or, more generally, of a trans- classes. A mother class Distribution is introduced formation of X, Y under a function f : R2 → R which with slots for a parameter and for methods r, d, p, is already realized as a function in R. and q, consecutively for simulation and for evalu- These operations are possible with the distr pack- ation of the density, c.d.f., and quantile function of age. As an example, try the corresponding distribution. All distributions of > require("distr") the base package are implemented as subclasses. By Loading required package: distr means of these classes, we can automatically gener- [1] TRUE ate new objects of these classes for the laws of r.v.’s > N <- Norm(mean=2,sd=1.3) under standard mathematical univariate transforma- > P <- Pois(lambda=1.2) tions and under convolution of independent r.v.’s. In > (Z <- 2*N+3+P) the distrSim and distrTEst packages, we also pro- Distribution Object of Class: AbscontDistribution vide classes for a standardized treatment of simula- > plot(Z) tions (also under contamination) and evaluations of > p(Z)(0.4) statistical procedures on such simulations. [1] 0.002415384 > q(Z)(0.3) [1] 6.70507 Motivation > r(Z)(10) [1] 11.072931 7.519611 10.567212 3.358912 R contains powerful techniques for virtually any use- [5] 7.955618 9.094524 5.293376 5.536541 ful distribution using the suggestive naming conven- [9] 9.358270 10.689527 tion [prefix]<name> as functions, where [prefix] stands for r, d, p, or q, and <name> is the name of the Density of AbscontDistribution CDF of AbscontDistribution Quantile of AbscontDistribution distribution. There are limitations of this concept: You can 30 only use distributions that are already implemented 0.12 0.8 in some library or for which you have provided an 20 implementation. In many natural settings you want grid 10 0.4 to formulate algorithms once for all distributions, so 0.06 d(x)(grid) p(x)(grid) you should be able to treat the actual distribution 0 <name> as if it were a variable. 0.0 You may of course paste together a prefix and 0.00 0 20 0 20 0.0 0.4 0.8 the value of <name> as a string and then use grid grid p(x)(grid) eval(parse(....)). This is neither very elegant nor flexible, however. Instead, we would prefer to implement the algo- Figure 1: density, c.d.f. and quantile function of Z rithm by passing an object of some distribution class as an argument to a function. Even better, though, Comment: we would use a generic function and let the S4- N and P are assigned objects of classes "Norm" and R News ISSN 1609-3631 Vol. 6/2, May 22, 2006 3 "Pois", respectively, with corresponding parameters. in R. They all have a param slot for a parameter, an The command Z <- 2*N+3+P generates a new distribu- img slot for the range of the corresponding r.v., and tion object and assigns it to Z — an object of class r, d, p, and q slots. "AbscontDistribution". Identifying N, P, Z with r.v.’s dis- L(Z) = L( N + tributed according to these distributions, 2 Subclasses 3 + P). Hence, p(Z)(0.4) returns P(Z ≤ 0.4); q(Z)(0.3) returns the 30%-quantile of Z; r(Z)(10) generates 10 At present, the distr package is limited to univari- pseudo random numbers distributed according to Z; and ate distributions; these are derived from the subclass the plot command produces Figure 1. UnivariateDistribution, and as typical subclasses, we introduce classes for absolutely continuous (a.c.) and discrete distributions — AbscontDistribution Concept and DiscreteDistribution. The latter has an extra slot support, a vector containing the sup- Our class design is deliberately open so that our port of the distribution, which is truncated to the classes may easily be extended by any volunteer in lower/upper TruncQuantile in case of an infinite the R community; loose ends are multivariate dis- support. TruncQuantile is a global option of distr tributions, time series distributions, and conditional described in Ruckdeschel et al. (2005, Section 4). distributions. As an exercise, the reader is encour- As subclasses we have implemented all paramet- aged to implement extreme value distributions from ric families from the base package simply by pro- package evd. The greatest effort will be the docu- viding wrappers to the original R-functions of form mentation. [prefix]<name>. More precisely, as subclasses of As to the naming of the slots, our goal was to pre- class AbscontDistribution, we have implemented serve naming and notation from the base package Beta, Cauchy, Chisq, Exp, Logis, Lnorm, Norm, Unif, as far as possible so that any programmer familiar and Weibull; to avoid name conflicts, the F-, t-, and with S could quickly use our distr package. In ad- Γ-distributions are implemented as Fd, Td, Gammad. dition, as the distributions already implemented in R As subclasses of class DiscreteDistribution, we are all well tested and programmed, we use the exist- have implemented Binom, Dirac, Geom, Hyper, ing r, d, p, and q-functions wherever possible, simply NBinom, and Pois. wrapping them in small code sniplets to our class hi- erarchy. All this should make intensive use of object Parameter classes orientation in order to be able to use inheritance and method overloading.
Load more

Recommended publications
  • Progress in the R Ecosystem for Representing and Handling Spatial Data
    Journal of Geographical Systems https://doi.org/10.1007/s10109-020-00336-0 ORIGINAL ARTICLE Progress in the R ecosystem for representing and handling spatial data Roger S. Bivand1 Received: 9 October 2019 / Accepted: 8 September 2020 © The Author(s) 2020 Abstract Twenty years have passed since Bivand and Gebhardt (J Geogr Syst 2(3):307–317, 2000. https ://doi.org/10.1007/PL000 11460 ) indicated that there was a good match between the then nascent open-source R programming language and environment and the needs of researchers analysing spatial data. Recalling the development of classes for spatial data presented in book form in Bivand et al. (Applied spatial data analysis with R. Springer, New York, 2008, Applied spatial data analysis with R, 2nd edn. Springer, New York, 2013), it is important to present the progress now occurring in representation of spatial data, and possible consequences for spatial data handling and the statistical analysis of spatial data. Beyond this, it is imperative to discuss the relationships between R-spatial software and the larger open-source geospatial software community on whose work R packages crucially depend. Keywords Spatial data analysis · Open-source software · R programming language JEL Classifcation C00 · C88 · R15 1 Introduction While Bivand and Gebhardt (2000) did provide an introduction to R as a statistical programming language and to why one might choose to use a scripted language like R (or Python), this article is both retrospective and prospective. It is possible that those approaching the choice of tools for spatial analysis and for handling spatial data will fnd the following less than inviting; in that case, perusal of early chapters of Lovelace et al.
    [Show full text]
  • Sweave User Manual
    Sweave User Manual Friedrich Leisch and R-core June 30, 2017 1 Introduction Sweave provides a flexible framework for mixing text and R code for automatic document generation. A single source file contains both documentation text and R code, which are then woven into a final document containing • the documentation text together with • the R code and/or • the output of the code (text, graphs) This allows the re-generation of a report if the input data change and documents the code to reproduce the analysis in the same file that contains the report. The R code of the complete 1 analysis is embedded into a LATEX document using the noweb syntax (Ramsey, 1998) which is usually used for literate programming Knuth(1984). Hence, the full power of LATEX (for high- quality typesetting) and R (for data analysis) can be used simultaneously. See Leisch(2002) and references therein for more general thoughts on dynamic report generation and pointers to other systems. Sweave uses a modular concept using different drivers for the actual translations. Obviously different drivers are needed for different text markup languages (LATEX, HTML, . ). Several packages on CRAN provide support for other word processing systems (see Appendix A). 2 Noweb files noweb (Ramsey, 1998) is a simple literate-programming tool which allows combining program source code and the corresponding documentation into a single file. A noweb file is a simple text file which consists of a sequence of code and documentation segments, called chunks: Documentation chunks start with a line that has an at sign (@) as first character, followed by a space or newline character.
    [Show full text]
  • Supplementary Materials
    Tomic et al, SIMON, an automated machine learning system reveals immune signatures of influenza vaccine responses 1 Supplementary Materials: 2 3 Figure S1. Staining profiles and gating scheme of immune cell subsets analyzed using mass 4 cytometry. Representative gating strategy for phenotype analysis of different blood- 5 derived immune cell subsets analyzed using mass cytometry in the sample from one donor 6 acquired before vaccination. In total PBMC from healthy 187 donors were analyzed using 7 same gating scheme. Text above plots indicates parent population, while arrows show 8 gating strategy defining major immune cell subsets (CD4+ T cells, CD8+ T cells, B cells, 9 NK cells, Tregs, NKT cells, etc.). 10 2 11 12 Figure S2. Distribution of high and low responders included in the initial dataset. Distribution 13 of individuals in groups of high (red, n=64) and low (grey, n=123) responders regarding the 14 (A) CMV status, gender and study year. (B) Age distribution between high and low 15 responders. Age is indicated in years. 16 3 17 18 Figure S3. Assays performed across different clinical studies and study years. Data from 5 19 different clinical studies (Study 15, 17, 18, 21 and 29) were included in the analysis. Flow 20 cytometry was performed only in year 2009, in other years phenotype of immune cells was 21 determined by mass cytometry. Luminex (either 51/63-plex) was performed from 2008 to 22 2014. Finally, signaling capacity of immune cells was analyzed by phosphorylation 23 cytometry (PhosphoFlow) on mass cytometer in 2013 and flow cytometer in all other years.
    [Show full text]
  • The Split-Apply-Combine Strategy for Data Analysis
    JSS Journal of Statistical Software April 2011, Volume 40, Issue 1. http://www.jstatsoft.org/ The Split-Apply-Combine Strategy for Data Analysis Hadley Wickham Rice University Abstract Many data analysis problems involve the application of a split-apply-combine strategy, where you break up a big problem into manageable pieces, operate on each piece inde- pendently and then put all the pieces back together. This insight gives rise to a new R package that allows you to smoothly apply this strategy, without having to worry about the type of structure in which your data is stored. The paper includes two case studies showing how these insights make it easier to work with batting records for veteran baseball players and a large 3d array of spatio-temporal ozone measurements. Keywords: R, apply, split, data analysis. 1. Introduction What do we do when we analyze data? What are common actions and what are common mistakes? Given the importance of this activity in statistics, there is remarkably little research on how data analysis happens. This paper attempts to remedy a very small part of that lack by describing one common data analysis pattern: Split-apply-combine. You see the split-apply- combine strategy whenever you break up a big problem into manageable pieces, operate on each piece independently and then put all the pieces back together. This crops up in all stages of an analysis: During data preparation, when performing group-wise ranking, standardization, or nor- malization, or in general when creating new variables that are most easily calculated on a per-group basis.
    [Show full text]
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • 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,
    [Show full text]
  • R Programming for Data Science
    R Programming for Data Science Roger D. Peng This book is for sale at http://leanpub.com/rprogramming This version was published on 2015-07-20 This is a Leanpub book. Leanpub empowers authors and publishers with the Lean Publishing process. Lean Publishing is the act of publishing an in-progress ebook using lightweight tools and many iterations to get reader feedback, pivot until you have the right book and build traction once you do. ©2014 - 2015 Roger D. Peng Also By Roger D. Peng Exploratory Data Analysis with R Contents Preface ............................................... 1 History and Overview of R .................................... 4 What is R? ............................................ 4 What is S? ............................................ 4 The S Philosophy ........................................ 5 Back to R ............................................ 5 Basic Features of R ....................................... 6 Free Software .......................................... 6 Design of the R System ..................................... 7 Limitations of R ......................................... 8 R Resources ........................................... 9 Getting Started with R ...................................... 11 Installation ............................................ 11 Getting started with the R interface .............................. 11 R Nuts and Bolts .......................................... 12 Entering Input .......................................... 12 Evaluation ...........................................
    [Show full text]
  • ESS — Emacs Speaks Statistics
    ESS — Emacs Speaks Statistics ESS version 5.14 The ESS Developers (A.J. Rossini, R.M. Heiberger, K. Hornik, M. Maechler, R.A. Sparapani, S.J. Eglen, S.P. Luque and H. Redestig) Current Documentation by The ESS Developers Copyright c 2002–2010 The ESS Developers Copyright c 1996–2001 A.J. Rossini Original Documentation by David M. Smith Copyright c 1992–1995 David M. Smith Permission is granted to make and distribute verbatim copies of this manual provided the copyright notice and this permission notice are preserved on all copies. Permission is granted to copy and distribute modified versions of this manual under the conditions for verbatim copying, provided that the entire resulting derived work is distributed under the terms of a permission notice identical to this one. Chapter 1: Introduction to ESS 1 1 Introduction to ESS The S family (S, Splus and R) and SAS statistical analysis packages provide sophisticated statistical and graphical routines for manipulating data. Emacs Speaks Statistics (ESS) is based on the merger of two pre-cursors, S-mode and SAS-mode, which provided support for the S family and SAS respectively. Later on, Stata-mode was also incorporated. ESS provides a common, generic, and useful interface, through emacs, to many statistical packages. It currently supports the S family, SAS, BUGS/JAGS, Stata and XLisp-Stat with the level of support roughly in that order. A bit of notation before we begin. emacs refers to both GNU Emacs by the Free Software Foundation, as well as XEmacs by the XEmacs Project. The emacs major mode ESS[language], where language can take values such as S, SAS, or XLS.
    [Show full text]
  • Conference Brochure S P E a K E R S
    04.07.2017 – 07.07.2017 www.user2017.brussels @useR_Brussels #user2017 CONFERENCE BROCHURE S P E A K E R S 2 As a student my use of R for data analyses was frowned upon - the suggestion was to stick with the existing software. P Access to R related literature was difficult. To admire R the very first R books one had to travel far, to the few enlightened universities. As a researcher I needed the help E of a secret floppy disk to dual boot into a GNU/Linux system with R, in order to escape from unnamed products. As a F starter in industry, people from unnamed companies made clear to my management that setting up R events for customers was A giving a ‘wrong signal to the market’. C Look how ‘wrong’ we have been... Fifteen years later R is shining brightly in the data science field. Students are E learning R before anything else and if statistics research is published without accompanying R package, it is considered a bad sign. Publishers have hundreds of books on display and major software houses are in a constant race against time to further integrate R into their product offerings. And now, more than 1000 people from all over the world have come to Brussels to discover the state of R at this conference. R has come a great way and the community that formed around the R project is and has always been critical to its success. The community has evolved in many ways and is active in many places throughout the year, but of all things the official useR conference still is the major community event of the year.
    [Show full text]
  • August 12-14, Dortmund, Germany
    AASC Austrian Association for Statistical Computing 2008 Program August 12-14, Dortmund, Germany useR! 2008, Dortmund, Germany 1 Contents Greetings and Miscellaneous 2 Maps 5 Social Program 8 Program 9 Tutorials 9 Schedule 10 List of Talks 14 2 useR! 2008, Dortmund, Germany Dear useRs, the following pages provide you with some useful information about useR! 2008, the R user con- ference, taking place at the Fakultät Statistik, Technische Universität Dortmund, Germany from 2008-08-12 to 2008-08-14. Pre-conference tutorials will take place on August 11. The confer- ence is organized by the Fakultät Statistik, Technische Universität Dortmund and the Austrian Association for Statistical Computing (AASC). Apart from challenging and likewise exciting scientific contributions we hope to offer you an attractive conference site and a pleasant social program. With best regards from the organizing team: Uwe Ligges (conference), Achim Zeileis (program), Claus Weihs, Gerd Kopp (local organiza- tion), Friedrich Leisch, and Torsten Hothorn Address / Contact Address: Uwe Ligges Fakultät Statistik Technische Universität Dortmund 44221 Dortmund Germany Phone: +49 231 755 4353 Fax: +49 231 755 4387 e-mail: [email protected] URL: http://www.R-Project.org/useR-2008/ Program Committee Micah Altman, Roger Bivand, Peter Dalgaard, Jan de Leeuw, Ramón Díaz-Uriarte, Spencer Graves, Leonhard Held, Torsten Hothorn, François Husson, Christian Kleiber, Friedrich Leisch, Andy Liaw, Martin Mächler, Kate Mullen, Ei-ji Nakama, Thomas Petzoldt, Martin Theus, and Heather Turner Conference Location Technische Universität Dortmund Campus Nord Mathematikgebäude / Audimax Vogelpothsweg 87 44227 Dortmund Conference Office Opening hours: Monday, August 11: 08:30–19:30 Tuesday, August 12: 08:30–18:30 Wednesday, August 13: 08:30–18:30 Thursday, August 14: 08:30–15:30 useR! 2008, Dortmund, Germany 3 Public Transport The conference site at the university campus and the city hall are best to be reached by public transport.
    [Show full text]
  • Species' Traits and Phylogenetic
    Northern Michigan University NMU Commons All NMU Master's Theses Student Works 8-2018 CLIMATE DRIVEN RANGE SHIFTS OF NORTH AMERICAN SMALL MAMMALS: SPECIES’ TRAITS AND PHYLOGENETIC INFLUENCES Katie Nehiba [email protected] Follow this and additional works at: https://commons.nmu.edu/theses Part of the Ecology and Evolutionary Biology Commons Recommended Citation Nehiba, Katie, "CLIMATE DRIVEN RANGE SHIFTS OF NORTH AMERICAN SMALL MAMMALS: SPECIES’ TRAITS AND PHYLOGENETIC INFLUENCES" (2018). All NMU Master's Theses. 557. https://commons.nmu.edu/theses/557 This Open Access is brought to you for free and open access by the Student Works at NMU Commons. It has been accepted for inclusion in All NMU Master's Theses by an authorized administrator of NMU Commons. For more information, please contact [email protected],[email protected]. CLIMATE DRIVEN RANGE SHIFTS OF NORTH AMERICAN SMALL MAMMALS: SPECIES’ TRAITS AND PHYLOGENETIC INFLUENCES By Katie R. Nehiba THESIS Submitted to Northern Michigan University In partial fulfillment of the requirements For the degree of MASTER OF SCIENCE Office of Graduate Education and Research July 2018 ABSTRACT By Katie R. Nehiba Current anthropogenically-driven climate change is accelerating at an unprecedented rate. In response, species’ ranges may shift, tracking optimal climatic conditions. Species-specific differences may produce predictable differences in the extent of range shifts. I evaluated if patterns of predicted responses to climate change were strongly related to species’ taxonomic identities and/or ecological characteristics of species’ niches, elevation and precipitation. I evaluated differences in predicted range shifts in well-sampled small mammals that are restricted to North America: kangaroo rats, voles, chipmunks, and ground squirrels.
    [Show full text]