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1 Dynamisches Tool Zur (Grafischen) Datenanalyse Statistik-Software Kommerzielle Software Freeware S-Plus www.insightful.com/products/splus/ R www.r-project.org/ SPSS www.spss.com/ PSPP www.gnu.org/software/pspp/pspp.html NCSS www.ncss.com/ Matlab www.mathworks.de/ Octave, Scilab www.scilab.org/ Eviews www.eviews.com PD Dr. Matthias Fischer Ox www.oxmetrics.net/ SAS www.sas.com/ [email protected] STATA www.stata.com/ LIMDEP www.limdep.com/ JMultiwww.jmulti.de/ Version 2.0 Matthias Fischer () Einf¨uhrung in R Version 2.0 1 / 95 Matthias Fischer () Einf¨uhrung in R Version 2.0 2 / 95 Warum R? R-Homepage http://www.r-project.org/ 1 Dynamisches Tool zur (grafischen) Datenanalyse 2 Taschenrechnerfunktion 3 Programmiersprache 4 Freeware-Version von S-PLUS 5 Alternative zu MATLAB, GAUSS, OX, etc. 6 Erg¨anzung zu Excel oder SPSS Download, Dokumentation, Newsgroups Matthias Fischer () Einf¨uhrung in R Version 2.0 3 / 95 Matthias Fischer () Einf¨uhrung in R Version 2.0 4 / 95 R-Installation 1/4 Start der Datei R-2.2.1-win32.exe Installation Matthias Fischer () Einf¨uhrung in R Version 2.0 5 / 95 Matthias Fischer () Einf¨uhrung in R Version 2.0 6 / 95 R-Installation 2/4 R-Installation 3/4 Matthias Fischer () Einf¨uhrung in R Version 2.0 7 / 95 Matthias Fischer () Einf¨uhrung in R Version 2.0 8 / 95 R-Installation 4/4 Literatur Matthias Fischer () Einf¨uhrung in R Version 2.0 9 / 95 Matthias Fischer () Einf¨uhrung in R Version 2.0 10 / 95 R-B¨ucher R-B¨ucher Everitt Behr An R and S-Plus Companion to Multivariate Analysis. Einf¨uhrung in die Statistik mit R. Springer-Verlag, Heidelberg, 2005. Verlag Vahlen, M¨unchen, 2005. Everitt/Hothorn Crawley A Handbook of Statistical Analyses using R. Statistics: An Introduction using R. Chapman & Hall/CRC, Boca Raton, 2006 Wiley, Chicester, 2005. Dol´ıc Faraway Statistik mit R. Extending Linear Models with R: Generalized Linear, Mixed Effects Oldenbourg, M¨unchen, 2004. and Nonparametric Regression Models. Chapman & Hall/CRC, Boca Raton, 2006. Dalgaard Introductory Statistics with R Series: Statistics and Computing. Ligges Springer, Berlin, 2004. Programmieren mit R. Springer-Verlag, Heidelberg, 2005. Matthias Fischer () Einf¨uhrung in R Version 2.0 11 / 95 Matthias Fischer () Einf¨uhrung in R Version 2.0 12 / 95 R-B¨ucher R-Skripten Verzani (2002) Maindonald/Braun simpleR - Using R for Introductory Statistics. Data Analysis and Graphics Using R - An Example-Based Approach. http://cran.r-project.org/doc/contrib/Verzani-SimpleR.pdf. Cambridge University Press, Cambridge, 2003. Murrell Paradis (2002) R Graphics. R for Beginners. Chapman & Hall/CRC, Boca Raton, 2005. http://cran.r-project.org/doc/contrib/Paradis-rdebuts en.pdf. Pfaff Faraway (2002) Analysis of Integrated and Cointegrated Time Series with R. Practical Regression and Anova using R. Springer, Heidelberg, 2006. http://cran.r-project.org/doc/contrib/Faraway-PRA.pdf. Verzani Handl (2005) Using R for Introductory Statistics. Einf¨uhrung in die Statistik mit R. Chapman & Hall/CRC, Boca Raton, 2005. http://www.wiwi.uni- bielefeld.de/∼frohn/Lehre/Statistik1/Skript/stat12b.pdf. Matthias Fischer () Einf¨uhrung in R Version 2.0 13 / 95 Matthias Fischer () Einf¨uhrung in R Version 2.0 14 / 95 R-Reference Cards Inhaltsverzeichnis 1. Besonderheiten & Bibliotheken Tom Short (2002) R Reference card. 2. R-Commander http://cran.r-project.org/doc/contrib/Short-refcard.pdf. 3. Objekte, Klassen, Operatoren und Funktionen John Baron (2004) R Reference card. http://cran.r-project.org/doc/contrib/refcard.pdf. 4. Import und Export von Daten Diethelm W¨urtz (2006) 5. Grafiken in R R/RMetrics Reference card. http://www.itp.phys.ethz.ch/econophysics/R/docs/DocRefcard.pdf. 6. Verteilungen in R Matthias Fischer () Einf¨uhrung in R Version 2.0 15 / 95 Matthias Fischer () Einf¨uhrung in R Version 2.0 16 / 95 Inhaltsverzeichnis 7. Das lineare Regressionsmodell in R 8. Erstellung eigener Funktionen Teil 1: Besonderheiten & Bibliotheken 9. Hilfe 10. R-Metrics 11. Zeitreihenanalyse mit R 12. Multivariate Verfahren mit R 13. Symbolisches Ableiten Matthias Fischer () Einf¨uhrung in R Version 2.0 17 / 95 Matthias Fischer () Einf¨uhrung in R Version 2.0 18 / 95 Command-Window Besonderheiten Nach dem Programmstart ... R unterscheidet zwischen Groß- und Kleinbuchstaben. Variablennamen ”c” bzw. ”t” sind schon belegt. Nachkommastellen werden durch Dezimalpunkt getrennt. Eingabebereitschaft wird durch ”>” signalisiert. Unvollst¨andige Eingabe wird durch ”+” signalisiert. Objekte werden nach Beenden von R nicht automatisch gespeichert. Befehlshistorie erh¨altman mittels ”↑”-Taste. Matthias Fischer () Einf¨uhrung in R Version 2.0 19 / 95 Matthias Fischer () Einf¨uhrung in R Version 2.0 20 / 95 Bibliotheken Bibliotheken Ein ”gewisser Vorrat” an Funktionen, Daten etc. ist automatisch nach dem Start verf¨ugbar. Name Kurzbeschreibung Speziellere Funktionen sind in den unz¨ahligen Zusatzpaketen (sog. evd Extremwertstatistik ”packages”) verf¨ugbar (http://stat.ethz.ch/CRAN/index.html). lmtest Diverse Testverfahren matrix Matrizen-Operationen Das Anzeigen der in der aktiven Sitzung bereits eingebundenen panel Analyse von Paneldaten Pakete erfolgt mittels search(). Rcmdr Grafische Oberfl¨ache f¨ur R uroot Einheitswurzeltests Potenzielle am Rechner zur Einbindung zur Verf¨ugung stehende tseries Zeitreihenanalyse VaR Value-at-Risk Berechnung Pakete k¨onnen mittels library() angezeigt werden. Table: Ausgew¨ahlte R-Bibliotheken Nicht vorhandene Zusatz-Packages m¨ussen zuerst installiert werden. Danach k¨onnensie durch den Befehl library(package.name) eingebunden werden, Information ¨uber das Paket dann durch library(help=package.name). Matthias Fischer () Einf¨uhrung in R Version 2.0 21 / 95 Matthias Fischer () Einf¨uhrung in R Version 2.0 22 / 95 Installation von Bibliotheken (via Internet) Installation von Bibliotheken (via CD) Men¨uleiste Pakete → Installiere Paket(e)... Men¨uleiste Pakete → Installiere Paket(e) aus lokalen ZIP-Datei... Matthias Fischer () Einf¨uhrung in R Version 2.0 23 / 95 Matthias Fischer () Einf¨uhrung in R Version 2.0 24 / 95 R-Commander Grafische Benutzeroberfl¨achevon R Teil 2: Der R-Commander Matthias Fischer () Einf¨uhrung in R Version 2.0 25 / 95 Matthias Fischer () Einf¨uhrung in R Version 2.0 26 / 95 Installation des R-Commanders 1 Starten Sie R. Installieren Sie die Pakete Rcmdr ebenso wie zoo, strucchange, sandwich, relimp, mvtnorm, multcomp, lmtest, effects, car und abind. Teil 3a: Objekte, Datentypen, Operatoren 2 Men¨uleiste ”Bearbeiten” → ”GUI Einstellungen...”. 3 Anklicken der Option ”SDI”. 4 Anklicken von ”Speichern” (2x) und ”OK” (2x). 5 Verlassen Sie R und starten Sie R neu. 6 Geben Sie dann library(Rcmdr) ein. Die Oberfl¨ache wird dann automatisch geladen. Matthias Fischer () Einf¨uhrung in R Version 2.0 27 / 95 Matthias Fischer () Einf¨uhrung in R Version 2.0 28 / 95 Datentypen in R Objekte in R Numerische Zahlen (”numeric”) Vektor: Anordnung gleichartiger Elemente (z.B. Zahlen) vector(mode = "logical", length = 0) → 1.7, π (Pi), +Inf, -Inf (±∞), NaN (”not a number”) as.vector(x, mode = "any") bzw. is.vector(x, mode = "any") numeric(length = 0), as.numeric(x), is.numeric(x) Faktor: Kategoriale Daten Komplexe Zahlen (”complex”) factor(x, levels = sort(unique.default(x), na.last = TRUE),labels = levels) → 1 + 2i is.factor(x) bzw. as.factor(x) complex(length.out = 0, real = numeric(), imaginary = numeric()), as.complex(x), is.complex(x), Re(x), Im(x), Mod(x) Matrix: Anordnung gleichartiger Elemente in Zeilen und Spalten matrix(data = NA, nrow = 1, ncol = 1, byrow = FALSE, dimnames = NULL) as.matrix(x) bzw. is.matrix(x) Zeichenketten (”string”) → "Hallo &7" Array: ”Matrix f¨urh¨ohereDimensionen” character(length = 0), as.character(x), is.character(x) array(data = NA, dim = length(data), dimnames = NULL) as.array(x) bzw. is.array(x) Wahrheitswerte (”logical”) Liste: Anordnung evtl. unterschiedlicher Objekte → TRUE bzw. T (”wahr”), FALSE/F (”falsch”), NA (”not available”) list(...) bzw. as.list(x, ...) bzw. is.list(x) logical(length = 0), as.logical(x), is.logical(x) Matthias Fischer () Einf¨uhrung in R Version 2.0 29 / 95 Matthias Fischer () Einf¨uhrung in R Version 2.0 30 / 95 Objekte in R Attribute von Objekten in R Datenframe: Anordnung unterschiedlicher Elemente gleicher L¨ange data.frame(...) bzw. is.data.frame bzw. as.data.frame Zeitreihe: zeitliche Anordnung von Beobachtungen einer Variablen → ”spezifizieren die Art der Daten n¨aher” (in Vektor) bzw. mehrerer Variablen (in Matrix) sowie Informationen ts(data = NA, start = 1, end = numeric(0), frequency = 1) Datentyp: mode(x) as.ts(x, ...) bzw. is.ts(x) L¨ange: length(x) Ausdr¨ucke (”expression”): Sinnvolle Ansammlung von Zeichen expression(...) bzw. as.expression(x, ...) bzw. is.expression(x) Klasse: class(x) Funktion (”function”): Sinnvolle Ansammlung von Zeichen function(x, ...) bzw. as.function(x, ...) bzw. is.function(x) Formel (”formula”): Sinnvolle Ansammlung von Zeichen formula(x, ...) bzw. as.formula(x, ...) Matthias Fischer () Einf¨uhrung in R Version 2.0 31 / 95 Matthias Fischer () Einf¨uhrung in R Version 2.0 32 / 95 Variablen in R Objektverwaltung in R Anzeigen aller Objekte im aktuellen Workspace mittels objects() Der Begriff einer Variablen in R ist sehr flexibel gefasst: Man versteht bzw. synonym ls(). darunter allgemein ein mit einem Namen (”Variablennamen”) ansprechbares Objekt. Anzeigen aller Objekte eines Pakets XX mittels objects("package:XX"). Variablen werden durch ihre erste Wertzuweisung mittels ”<-” oder ”=” initialisiert: a<-3 bzw. a=3. Anzeigen aller Objekte im aktuellen Workspace mittels die ein ”b” im Namen enthalten mittels ls(pat="b"). R unterscheidet zwischen Klein- und Großbuchstaben bei Variablennamen: aaa=3 ungleich AAA=3. L¨oschen eines Objekte X mittels rm(X) bzw. remove(X). Achtung: R ¨uberschreibt Variablen ohne Nachfrage. L¨oschen aller Objekte im aktuellen Workspace mittels rm(list=ls()). Matthias Fischer () Einf¨uhrung in R Version 2.0 33 / 95 Matthias Fischer () Einf¨uhrung in R Version 2.0 34 / 95 [Mathematische] Operatoren in R Vergleichsoperatoren in R → Verkn¨upfung von Zahlen, Konstanten, Vektoren bzw.
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