R For Sas And Spss Users Download

R is a powerful and free software system for data analysis and graphics, with over 5,000 add-on packages available. This book introduces R using SAS and SPSS terms with which you are already familiar. It demonstrates which of the add-on packages are most like SAS and SPSS and compares them to R's built-in functions. It steps through over 30 programs written in all three packages, comparing and contrasting the packages' differing approaches. The programs and practice datasets are available for download. The glossary defines over 50 R terms using SAS/SPSS jargon and again using R jargon. The table of contents and the index allow you to find equivalent R functions by looking up both SAS statements and SPSS commands. When finished, you will be able to import data, manage and transform it, create publication quality graphics, and perform basic statistical analyses. This new edition has updated programming, an expanded index, and even more statistical methods covered in over 25 new sections.

• Robert A Muenchen

Norman Nie, one of the founders of SPSS, calls R [55] "The most powerful statistical computing language on the planet."1 Written by Ross Ihaka, Robert Gentleman, the R Core Development Team, and an army of volunteers, R provides both a language and a vast array of analytical and graphical procedures. The fact that this level of power is available free of charge has dramatically changed the landscape of research software.

• Robert A Muenchen

There are several ways you can run R: Interactively using its programming language: You can see the result of each command immediately after you submit it; Interactively using one of several GUIs that you can add on to R: Some of these use programming while others help you avoid programming by using menus and dialog boxes like SPSS, ribbons like Microsoft Office, or flowcharts like SAS Enterprise Guide or SPSS Modeler (formerly Clementine); Noninteractively in batch mode using its programming language: You enter your program into a file and run it all at once. From within another package, such as Excel, SAS, or SPSS.

• Robert A Muenchen

In this chapter we will go through the fundamental features in R. It will be helpful if you can download the book's files from the Web site http://r4stats.com and run each line as we discuss it. Many of our examples will use our practice data set described in Sect. 1.7.

• Robert A Muenchen

You can enter data directly into R, and you can read data from a wide range of sources. In this chapter I will demonstrate R's data editor as well as reading and writing data in text, Excel, SAS, SPSS and ODBC formats. For other topics, especially regarding relational databases, see the R Data Import/Export manual [46]. If you are reading data that contain dates or times, see Sect. 10.21.

• Robert A Muenchen

In SAS and SPSS, selecting variables for an analysis is simple, while selecting observations is often much more complicated. In R, these two processes can be almost identical. As a result, variable selection in R is both more flexible and quite a bit more complex. However, since you need to learn that complexity to select observations, it does not require much added effort.

• Robert A Muenchen

It bears repeating that the approaches that R uses to select observations are, for the most part, the same as those discussed in the previous chapter for selecting variables. This chapter builds on that one, so if you have not read it recently, now would be a good time to do so.

• Robert A Muenchen

As we have seen, compared to SAS or SPSS, R output is quite sparse and not nicely formatted for word processing. You can improve R's output by adding value and variable labels. You can also format the output to make beautiful tables to use with word processors, Web pages, and document preparation systems.

• Robert A Muenchen

When using SAS and SPSS, you manage your files using the same operating system commands that you use for your other software. SAS does have a few file management procedures such as DATASETS and CATALOG, but you can get by just fine without them for most purposes.

• Robert A Muenchen

Graphics is perhaps the most difficult topic to compare across SAS, SPSS, and R. Each package contains at least two graphical approaches, each with dozens of options and each with entire books devoted to them. Therefore, we will focus on only two main approaches in R, and we will discuss many more examples in R than in SAS or SPSS. This chapter focuses on a broad, high-level comparison of the three. The next chapter focuses on R's traditional graphics. The one after that focuses just on the grammar of graphics approaches used in both R and SPSS.

• Robert A Muenchen

In the previous chapter, we discussed the various graphics packages in R, SAS, and SPSS. Now we will delve into R's traditional, or base, graphics. Many of these examples will use the practice data set mydata100, which is described in Sect. 1.7

• Robert A Muenchen

As we discussed in Chap. 14, "Graphics Overview," the ggplot2 package is an implementation of Wilkinson's grammar of graphics (hence the "gg" in its name). The last chapter focused on R's traditional graphics functions. Many plots were easy, but other plots were a lot of work compared to SAS or SPSS. In particular, adding things like legends and confidence intervals was complicated.

• Robert A Muenchen

This chapter demonstrates some basic statistical methods. More importantly, it shows how even in the realm of fairly standard analyses, R differs sharply from the approach used by SAS and SPSS. Since this book is aimed at people who already know SAS or SPSS, I assume you are already familiar with most of these methods. I briefly list each test's goal and assumptions and how to get R to perform them. For more statistical coverage see Dalgaard's Introductory Statistics with R [16], or Venable and Ripley's much more advanced Modern Applied Statistics with S [65].

• Robert A Muenchen

As we have seen, R differs from SAS and SPSS in many ways. R has a host of features that the other programs lack such as functions whose internal workings you can see and change, fully integrated macro and matrix capabilities, the most extensive selection of analytic methods available, and a level of flexibility that extends all the way to the core of the system. A detailed comparison of R with SAS and SPSS is contained in Appendix B.

... The R system is a free software environment for statistical computing and graphics distributed under a GNU-style copyleft license and running under Unix, Windows and Mac (R Development Core Team, 2009). Several documents and books provide an introduction (Dalgaard, 2008;Venables et al., 2009;Muenchen, 2009). The add-on package surveillance offers functionality for the visualization, monitoring and simulation of count data time series in R for public health surveillance and biosurveillance. ...

... There are options for this kind of transfer or exchange. Firstly, there are many guides offering syntactic cross-walk between R and SPSS, SAS, Matlab, etcetera (Muenchen and Hilbe, 2010;Muenchen, 2011;Kleinman and Horton, 2014). But there are also some built-in codeworks that allow for ease of transition. ...

Researchers engaged in the scholarship of teaching and learning seek tools for rigorous, quantitative analysis. Here we present a brief introduction to computational techniques for the researcher with interest in analyzing data pertaining to pedagogical study. Sample dataset and fully executable code in the open-source R programming language are provided, along with illustrative vignettes relevant to common forms of inquiry in the educational setting.

... Arguably, R's programming language is designed for and by statisticians, so the language often offers users benefits and efficiencies over GUIs. A variety of texts exist, which effectively teach and guide readers to use the R programming language for the most commonly used statistical methods (Dalgaard, 2002;Maindonald & Braun, 2003;Muenchen, 2009), as well as books for more specific statistical topics such as time series or statistical computing (Cowpertwait & Metcalfe, 2009;Rizzo, 2008). Several articles also provide a general overview of R for new users (Cribari-Neto & Zarkos, 1999;Racine & Hyndman, 2002). ...

• Steven Andrew Culpepper
• Herman Aguinis

The authors review the open source statistical package R. R allows researchers to implement statistical techniques including linear modeling, linear and nonlinear multilevel modeling, factor and principal component analysis, structural equation modeling, item and reliability analysis, time series modeling, and meta-analysis, among others. R presents several advantages over other statistical packages because it is updated on an ongoing basis, is free, is capable of creating high-quality graphics that are difficult to create with other packages, and includes important simulation capabilities. Some limitations of R include the need to learn a new programming language, difficulties handling missing data for new users, and relatively limited support and documentation. R is not yet popular in the organizational sciences but, given its ongoing improvement and many positive features, we predict that it will soon be.

... The R package " mice " was favored for multiple imputation for missing data because it offers a multilevel multiple imputation for missing data algorithm (Van Buuren & Grootshuis-Outdhoorn, 2011; Yucel, 2011). The R package was invoked from SAS 9.3 statistical software for data generation and analysis of the imputed data (Muenchen, 2011; SAS Institute, Cary, NC, 2011). In particular, the missing data procedure " proc mianalyze " combined the results of the statistical analyses of imputations to produce valid statistical inferences. ...

It is essential for research funding organizations to ensure both the validity and fairness of the grant approval procedure. The ex-ante peer evaluation (EXANTE) of N = 8,496 grant applications submitted to the Austrian Science Fund from 1999 to 2009 was statistically analyzed. For 1,689 funded research projects an ex-post peer evaluation (EXPOST) was also available; for the rest of the grant applications a multilevel missing data imputation approach was used to consider verification bias for the first time in peer-review research. Without imputation, the predictive validity of EXANTE was low (r = .26) but underestimated due to verification bias, and with imputation it was r = .49. That is, the decision-making procedure is capable of selecting the best research proposals for funding. In the EXANTE there were several potential biases (e.g., gender). With respect to the EXPOST there was only one real bias (discipline-specific and year-specific differential prediction). The novelty of this contribution is, first, the combining of theoretical concepts of validity and fairness with a missing data imputation approach to correct for verification bias and, second, multilevel modeling to test peer review-based funding decisions for both validity and fairness in terms of potential and real biases.

... Bere aldetik, zuzenean kontsolan idatziz... > apropos ("vector") ...komatxoen artean jarritako testua barneratzen duten funtzio guztien zerrendara heltzen gara. (Muenchen, 2009). ...

• Daniel Wollschläger

Die folgenden Abschnitte sollen gleichzeitig die grundlegenden Datenstrukturen in R sowie Möglichkeiten zur deskriptiven Datenauswertung erläutern. Die Reihenfolge der Themen ist dabei so gewählt, dass die abwechselnd vorgestellten Datenstrukturen und darauf aufbauenden deskriptiven Methoden nach und nach an Komplexität gewinnen.

Texts and software that we are currently using for teaching multivariate analysis to non-statisticians lack in the delivery of confirmatory factor analysis (CFA). The purpose of this paper is to provide educators with a complement to these resources that includes CFA and its computation. We focus on how to use CFA to estimate a "composite reliability" of a psychometric instrument. This paper provides guidance for introducing, via a case-study, the non-statistician to CFA. As a complement to our instruction about the more traditional SPSS, we successfully piloted the software R for estimating CFA on nine non-statisticians. This approach can be used with healthcare graduate students taking a multivariate course, as well as modified for community stakeholders of our Center for American Indian Community Health (e.g. community advisory boards, summer interns, & research team members). The placement of CFA at the end of the class is strategic and gives us an opportunity to do some innovative teaching: (1) build ideas for understanding the case study using previous course work (such as ANOVA); (2) incorporate multi-dimensional scaling (that students already learned) into the selection of a factor structure (new concept); (3) use interactive data from the students (active learning); (4) review matrix algebra and its importance to psychometric evaluation; (5) show students how to do the calculation on their own; and (6) give students access to an actual recent research project.

Planned missing data designs allow researchers to increase the amount and quality of data collected in a single study. Unfortunately, the effect of planned missing data designs on power is not straightforward. Under certain conditions using a planned missing design will increase power, whereas in other situations using a planned missing design will decrease power. Thus, when designing a study utilizing planned missing data researchers need to perform a power analysis. In this article, we describe methods for power analysis and sample size determination for planned missing data designs using Monte Carlo simulations. We also describe a new, more efficient method of Monte Carlo power analysis, software that can be used in these approaches, and several examples of popular planned missing data designs.

• Max Kuhn
• Kjell Johnson

To begin Part I of this work, we present a simple example that illustrates the broad concepts of model building. Section 2.1 provides an overview of a fuel economy data set for which the objective is to predict vehicles' fuel economy based on standard vehicle predictors such as engine displacement, number of cylinders, type of transmission, and manufacturer. In the context of this example, we explain the concepts of "spending" data, estimating model performance, building candidate models, and selecting the optimal model (Section 2.2).

• Max Kuhn
• Kjell Johnson

When modeling discrete classes, the relative frequencies of the classes can have a significant impact on the effectiveness of the model. An imbalance occurs when one or more classes have very low proportions in the training data as compared to the other classes. Imbalance can be present in any data set or application, and hence, the practitioner should be aware of the implications of modeling this type of data. To illustrate the impacts and remedies for severe class imbalance, we present a case study example (Section 16.1) and the impact of class imbalance on performances measures (Section 16.2). Sections 16.3-16.6 describe approaches for handling imbalance using the existing data such as maximizing minority class accuracy, adjusting classification cut-offs or prior probabilities, or adjusting sample weights prior to model tuning. Handling imbalance can also be done through sophisticated up- or down-sampling methods (Section 16.7) or by applying costs to the classification errors (Section 16.8). In the Computing Section (16.9) we demonstrate how to implement these remedies in R. Finally, exercises are provided at the end of the chapter to solidify the concepts.

• Max Kuhn
• Kjell Johnson

In this chapter we discuss several models, all of which are akin to linear regression in that each can directly or indirectly be written in the widely know multiple linear regression form. We begin this chapter by describing a chemistry case study data set (Section 6.1) which will be used to illustrate models throughout this chapter as well as for Chapters 7-9. As a foundational model, we discuss ordinary linear regression (Section 6.2). Section 6.3 defines and illustrates partial least squares and its algorithmic and computational variations. Penalized models such as ridge regression, the lasso, and the elastic net are presented in Section 6.4. In the Computing Section (6.5) we demonstrate how to train each of these models in R. Finally, exercises are provided at the end of the chapter to solidify the concepts.

• Max Kuhn
• Kjell Johnson

Several of the preceding chapters have focused on technical pitfalls of predictive models, such as over-fitting and class imbalances. Often, true success may depend on aspects of the problem that are not directly related to the model itself. This chapter discusses topics such as: Type III errors (answering the wrong question, Section 20.1), the effect of unwanted noise in the response (Section 20.2) and in the predictors (Section 20.3), the impact of discretizing continuous outcomes (Section 20.4), extrapolation (Section 20.5), and the impact of a large number of samples (Section 20.6). In the Computing Section (20.7) we illustrate the implementation of an algorithm for determining samples' similarity to the training set. Finally, exercises are provided at the end of the chapter to solidify the concepts.

• Denis Pedyash
• Chunsheng Shi
• Alexey V. Belov

The paper illustrates adoption of innovation, Competitive Intelligence and Business Intelligence in unit of organizational structure. In practice, occurs a problem of administration, such as position allocation, assignment to positions, as well as arises a question: which of researched organizational structure unit is responsible for adaptation of decision making in cross-cultural environment. In the period of 2012-2014 the authors had been researching problems and experience of organization structure in Russian industrial enterprises based in China. The paper helps to understand, which of the positions (in this article we are talking about positions, related to innovation decision making, BI and CI) in international corporations are optimal for Canadian applicants, and which position are more suitable for Chinese employees. For more detailed review, it was decided to compare Canadian and Russian industrial enterprises, carrying on economical and business activity in China.

• George Spilich
• Washington College

One might think that teaching statistics to undergraduates and graduates in Psychology is as exciting as watching paint dry. Nothing could be further from the truth; in fact, these are exciting times for those of us with the happy responsibility of teaching statistics to the next generation of professionals. A revolution in how we teach statistics is occurring at this very moment. To fully appreciate the magnitude of this revolution, a bit of history on the revolutions that came before is in order.

• Daniel Wollschläger

Die Survival-Analyse modelliert Überlebenszeiten (Hosmer Jr, Lemeshow, & May, 2008; Klein & Moeschberger, 2003). Diese geben allgemein an, wieviel Zeit bis zum Eintreten eines bestimmten Ereignisses verstrichen ist und sollen hier deshalb gleichbedeutend mit Ereigniszeiten sein. Es kann sich dabei etwa um die Zeitdauer handeln, die ein Patient nach einer Behandlung weiter am Leben ist, um die verstrichene Zeit, bis ein bestimmtes Bauteil im Gebrauch einen Defekt aufweist, oder um die Dauer, die ein Kleinkind benötigt, um ein vordefiniertes Entwicklungsziel zu erreichen – z. B. einen Mindestwortschatz besitzt. Bei der Analyse von Überlebenszeiten kann sowohl die Form ihres grundsätzlichen Verlaufs von Interesse sein, als auch inwiefern ihr Verlauf systematisch von Einflussgrößen abhängt.

• Daniel Wollschläger

Wenn inferenzstatistische Tests zur Datenauswertung herangezogen werden sollen, aber davon ausgegangen werden muss, dass strenge Anforderungen an die Art und Qualität der erhobenen Daten nicht erfüllt sind, kommen viele konventionelle Verfahren womöglich nicht in Betracht. Dagegen haben nonparametrische Methoden weniger restriktive Voraussetzungen und kommen auch bei kleinen Stichproben in Frage (Bortz, Lienert, & Boehnke, 2010; Büning & Trenkler, 1994).

• Daniel Wollschläger

Daten lassen sich in R mit Hilfe einer Vielzahl von Diagrammtypen grafisch darstellen, wobei hier nur auf eine Auswahl der verfügbaren Typen eingegangen werden kann. Für eine umfassende Dokumentation vgl. Murrell (2011) und Unwin (2015).

The goal of this work was to calculate landscape ecology metrics using the R language, allowing the analysis of forest fragments under the Atlantic Forest domain located in the sub-basin of Arroio Jaquirana, Rio Grande do Sul, Brazil. For the mapping of the forest fragments, we used images from the REIS/RapidEye sensor dated 2016, and the classification was supervised through the Bhattacharya algorithm. The fragments were analyzed in seven size classes, to separate them and to calculate the landscape metrics it was used R language. The results attained demonstrated that the native forest occupied 34.01% of the study area, covering a total of 1,995 fragments, of which 93.43% were less than 5 ha. The highest values of edge and perimeter-area ratio were found in the small fragments indicating a greater edge effect, with the central areas of these remnants being exposed to the external matrix effects. Thus, it is concluded that the Atlantic Forest is highly fragmented and is extremely important to establish measures to minimize the effects and/or increase the connectivity between the fragments through ecological corridors using the smaller fragments, in addition, it makes necessary the development of public policies and research for the management of the region in order to preserve the remnants.

• Daniel Wollschläger

Für Datenanalysen, die über wenige Teilschritte hinausgehen, ist die interaktive Arbeitsweise direkt auf der Konsole meist nicht sinnvoll. Stattdessen lässt sich die Auswertung automatisieren, indem alle Befehle zunächst zeilenweise in eine als Skript bezeichnete Textdatei geschrieben werden, die dann ihrerseits von R komplett oder in Teilen ausgeführt wird. Analoges gilt für die Verwaltung empirischer Daten: Gewöhnlich werden diese nicht von Hand auf der Konsole eingegeben, sondern in separaten Dateien gespeichert – sei es in R, in Programmen zur Tabellenkalkulation oder in anderen Statistikpaketen. Siehe Abschn. 4.3 für die Form der Pfadangaben zu Dateien in den folgenden Abschnitten.

• Daniel Wollschläger

Häufig bestehen in empirischen Untersuchungen Hypothesen über Erwartungswerte von Variablen. Viele der für solche Hypothesen geeigneten Tests gehen davon aus, dass bestimmte Annahmen über die Verteilungen der Variablen erfüllt sind, dass etwa in allen Bedingungen Normalverteilungen mit derselben Varianz vorliegen. Bevor auf Tests zum Vergleich von Erwartungswerten eingegangen wird, sollen deshalb zunächst jene Verfahren vorgestellt werden, die sich mit der Prüfung statistischer Voraussetzungen befassen (Abschn. 10.1). Für die statistischen Grundlagen dieser Themen vgl. Eid et al. (2015); Kirk (2013) sowie Maxwell und Delaney (2004).

• Daniel Wollschläger

R bietet nicht nur Mittel zur numerischen und grafischen Datenanalyse, sondern ist gleichzeitig eine Programmiersprache, die dieselbe Syntax wie die bisher behandelten Auswertungen verwendet. Das sehr umfangreiche Thema der Programmierung mit R soll in den folgenden Abschnitten nur soweit angedeutet werden, dass nützliche Sprachkonstrukte wie z. B. Kontrollstrukturen verwendet sowie einfache Funktionen selbst erstellt und analysiert werden können. Eine ausführliche Behandlung sei der hierauf spezialisierten Literatur überlassen (Chambers, 2008; Ligges, 2016; Wickham, 2014). Die Entwicklung eigener R-Pakete behandeln R Core Team (2016d) und Wickham (2015).

• Daniel Wollschläger

Mit dem Zusatzpaket ggplot2 lassen sich die in Kap. 14 vorgestellten Diagrammtypen ebenfalls erstellen. Dabei ist die Herangehensweise eine grundsätzlich andere: Während der Basisumfang von R für verschiedene Diagrammarten einzelne Funktionen bereitstellt, werden mit ggplot2 alle Diagrammtypen mit einem einheitlichen System erzeugt. Sind Diagramme des Basisumfangs analog zu einer Leinwand, auf der jede Funktion später nicht mehr änderbare Elemente aufmalt, repräsentiert ggplot2 alle Diagrammelemente explizit in einem Objekt. Erstellte Diagramme lassen sich über dieses Objekt weiter verändern, an Funktionen übergeben und speichern.

• Daniel Wollschläger

Liegen von Beobachtungsobjekten Werte mehrerer Variablen vor, kann sich die Datenanalyse nicht nur auf jede Variable einzeln, sondern auch auf die gemeinsame Verteilung der Variablen beziehen. Solche Fragestellungen sind mit multivariaten Verfahren zu bearbeiten (Backhaus, Erichson, Plinke, & Weiber, 2015a Backhaus, Erichson, & Weiber, 2015b; Mardia, Kent, & Bibby, 1980), deren Anwendung in R Zelterman (2015) vertiefend behandelt. Abschn. 14.6.8, 14.7 und 15.3 thematisieren Möglichkeiten, multivariate Daten in Diagrammen zu veranschaulichen.

• Daniel Wollschläger

Resampling-Verfahren kommen für eine Vielzahl von Tests in Frage, können hier aber nur in Grundzügen vorgestellt werden. Ausgangspunkt ist die gesuchte Verteilung einer Teststatistik \(\hat{\theta }\) – etwa eines Schätzers \(\hat{\theta }\) für einen theoretischen Parameter θ.

• Daniel Wollschläger

Die Korrelation zweier quantitativer Variablen ist ein Maß ihres linearen Zusammenhangs. Auch die lineare Regression analysiert den linearen Zusammenhang von Variablen, um die Werte einer Zielvariable (Kriterium) durch die Werte anderer Variablen (Prädiktoren, Kovariaten, Kovariablen) vorherzusagen. Für die statistischen Grundlagen dieser Themen vgl. die darauf spezialisierte Literatur (Eid et al., 2015), die auch für eine vertiefte Behandlung von Regressionsanalysen in R verfügbar ist (Faraway, 2014; Fox & Weisberg, 2011).

• Daniel Wollschläger

Vektoren, Matrizen und arrays sind dahingehend eingeschränkt, dass sie gleichzeitig nur Werte desselben Datentyps aufnehmen können. Da in empirischen Erhebungen meist Daten unterschiedlichen Typs – etwa numerische Variablen, Faktoren und Zeichenketten – anfallen, sind sie nicht unmittelbar geeignet, vollständige Datensätze zu speichern. Objekte der Klasse list und data.frame sind in dieser Hinsicht flexibler: Sie erlauben es, gleichzeitig Variablen unterschiedlichen Datentyps und auch unterschiedlicher Klasse als Komponenten zu besitzen.

• Daniel Wollschläger

Da empirische Daten fehlerbehaftet sind, bezieht die Anpassung eines statistischen Modells immer auch die Messfehler mit ein, die Parameterschätzungen orientieren sich daher zu stark an den zufälligen Besonderheiten der konkreten Stichprobe (overfitting). Die Güte der Passung des Modells lässt sich als Funktion \(f(\cdot )\) der Abweichungen \(E = Y -\hat{ Y }\) der Modellvorhersage \(\hat{Y }\) zu den tatsächlichen Werten der vorhergesagten Variable Y quantifizieren. Genauer soll \(\hat{Y }_{X,Y }\, (X')\) die folgende Vorhersage bezeichnen: Zunächst wird ein Modell an einer Stichprobe mit Werten für Prädiktoren X und Zielvariable Y (Kriterium) angepasst. In die Vorhersagegleichung mit den Parameterschätzungen dieses Modells werden dann (potentiell andere) Prädiktorwerte X′ eingesetzt, um die Vorhersage \(\hat{Y }\) zu berechnen, die mit den tatsächlichen Beobachtungen Y ′ zu vergleichen sind. f(E) ist die Verlustfunktion, die alle individuellen absoluten Abweichungen e i auf einen Gesamtwert für die Vorhersagegenauigkeit abbildet.

• Daniel Wollschläger

Bevor in den kommenden Kapiteln Funktionen zur inferenzstatistischen Datenanalyse besprochen werden, ist es notwendig Hilfsmittel vorzustellen, auf die viele dieser Funktionen zurückgreifen.

• Daniel Wollschläger

R ist eine freie und kostenlose Umgebung zur computergestützten statistischen Datenverarbeitung (Ihaka & Gentleman, 1996; R Core Team, 2014): R integriert eine Vielzahl von Möglichkeiten, um Daten organisieren, transformieren, auswerten und visualisieren zu können. Dabei bezeichnet R sowohl das Programm selbst als auch die Sprache, in der die Auswertungsbefehle geschrieben werden. Denn in R bestehen Auswertungen aus einer Abfolge von Befehlen in Textform, die der Benutzer unter Einhaltung einer bestimmten Syntax selbst einzugeben hat. Jeder Befehl stellt dabei einen eigenen Auswertungsschritt dar, wobei eine vollständige Datenanalyse die Abfolge vieler solcher Schritte umfasst. So könnten Daten zunächst aus einer Datei gelesen und zwei Variablen zu einer neuen verrechnet werden, ehe eine Teilmenge von Beobachtungen ausgewählt und mit ihr ein statistischer Test durchgeführt wird, dessen Ergebnisse im Anschluss grafisch aufzubereiten sind.

• Daniel Wollschläger

Numerische Methoden spielen in der Datenanalyse u. a. deswegen eine wichtige Rolle, weil nur in Spezialfällen geschlossene Formeln für die Parameterschätzung existieren, die zu einer bestmöglichen Passung eines statistischen Modells für beobachtete Daten führt. Der Einsatz numerischer Methoden bleibt dem Anwender aber verborgen, weil die typischerweise eingesetzten Funktionen zur Modellanpassung zwar intern auf solchen Methoden beruhen, sie die gewählten Algorithmen dem Anwender aber nicht unmittelbar offen legen.

• Johannes Breuer

R is a programming language and a freely available open-source software environment for statistical analyses and graphics (see http://www.r-project.org/). Besides being free and open-source, R has recently seen a surge in popularity mainly for its graphical capabilities for producing publication-ready tables and figures, and its functionalities for advanced multivariate analyses, such as structural equation and multilevel modeling. Graphical user interfaces (GUIs), such as RStudio ( http://www.rstudio.com/), can facilitate the use of R for users who are more familiar with statistical packages like SPSS, SAS, or Stata.

• Daniel Wollschläger

Das Modell der linearen Regression und Varianzanalyse (Abschn. 6.3, 7.3 und 12.9.1) lässt sich zum verallgemeinerten linearen Modell (GLM, generalized linear model) erweitern, das auch für Daten einer kategorialen vorherzusagenden Variable Y geeignet ist. Als Prädiktoren lassen sich sowohl kontinuierliche Variablen als auch Gruppierungsfaktoren einsetzen. Ein Spezialfall ist die logistische Regression für dichotome Y (codiert als 0 und 1). Im Vergleich zur Vorhersage quantitativer Variablen in der linearen Regression wird an diesem Beispiel zunächst folgende Schwierigkeit deutlich (für Details vgl. Faraway, 2016; Fox & Weisberg, 2011):

GGobi is a direct descendant of XGobi, with multiple plotting windows, a color lookup table manager, an XML (Extended Markup Language) file format for data, and other changes. Perhaps the biggest change is that GGobi can be embedded in other software and controlled using an API (Application Programming Interface). This design has been developed and tested in partnership with R. When GGobi is used with R, the result is a full marriage between GGobi's direct manipulation graphical environment and R's familiar extensible environment for statistical data analysis.

• Lionel Wickham

'Tritheism', in the sense of acknowledgement of three divine substances (ousiae), became a problem to the nonconforming Churches of Syria and Egypt during the sixth century. The origin and setting of the problem are briefly discussed and analysed. Damian Pope of Alexandria and Peter Patriarch of Antioch disagreed about the rebuttal of the doctrine and in particular about the claim by Damian that the tradition stemming from the Cappadocian Fathers legitimised the notion of the persons of the Trinity as 'subsistent relations'. The arguments on both sides are summarised and a resolution is suggested for speculation. The author notes the present relevance of the ancient debate both to current Trinitarian understanding affecting Christian ecclesiology and, also, to the growing discussion between Muslims and Christians about the Trinitarian idea and the doctrine of God that divides the religions, an area in which, the author argues, it is too facile, too indifferent to history, to say without qualification that both affirm the same God.

Source: https://www.researchgate.net/publication/235701552_R_for_SAS_and_SPSS_Users

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