Resampling-Verfahren

Wollschläger, Daniel

doi:10.1007/978-3-662-53670-4_11

Daniel Wollschläger⁴

Part of the book series: Statistik und ihre Anwendungen ((STATIST))

17k Accesses

Zusammenfassung

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Chapter: USD 29.95; Price excludes VAT (USA)

eBook: USD 44.99; Price excludes VAT (USA)

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

1.
θ ist ein Funktional der theoretischen Verteilungsfunktion F der ursprünglichen Zufallsvariable, bildet also F auf eine Zahl ab. Analog ist $\hat{\theta }$ dasselbe Funktional der empirischen kumulativen Häufigkeitsverteilung $\hat{F}_{n}$ der Basisstichprobe vom Umfang n und $\hat{\theta }^{\star }$ dasselbe Funktional der empirischen kumulativen Häufigkeitsverteilung $\hat{F}_{n}^{\star }$ in einer Replikation.
2.
Flexible bootstrap-basierte Tests für eine oder zwei Stichproben ermöglicht auch das Paket resample (Hesterberg, 2015).
3.
Die Indizes sind hier trotz der $999$ Replikationen nicht ganzzahlig (25 und 975), da die dem BC _a-Intervall zugrundeliegende Korrektur über die Verschiebung der Intervallgrenzen funktioniert. Vergleiche etwa das Perzentil-Intervall für θ ₁ aus boot.ci(bsRegr, conf=0.95, type="perc", index=1)$percent.
4.
Der p-Wert kann bei Monte-Carlo-Approximationen zur höheren Genauigkeit nach Hinzufügen eines zusätzlichen extremeren Falles gebildet werden: Ist n _R die Anzahl der generierten resamples und $n^{\star }$ die Anzahl der Fälle, bei denen $\hat{\theta }^{\star }$ mindestens so extrem wie $\hat{\theta }$ ist, setzt man $p = \frac{n^{\star }+1} {n_{R}+1}$. Auf diese Weise wird vermieden, dass der p-Wert exakt 0 werden kann.
5.
Formal muss das Kriterium der Austauschbarkeit erfüllt sein (Good, 2004).
6.
Auch die Pakete resample und vegan (Oksanen et al., 2016) bieten flexible Möglichkeiten, um Permutationstests für verschiedenen Untersuchungs-Designs umzusetzen.
7.
Eine weitere Alternative ist der van der Waerden-Test, für den die ursprünglichen Werte durch die zugehörigen Quantile aus der Standardnormalverteilung ersetzt werden. Dieser Test lässt sich mit normal_test() aus dem Paket coin umsetzen.
8.
Für deren Wahl vgl. vignette("coin_implementation").

Literatur

Adler, D., & Murdoch, D. (2016). rgl: 3D visualization device system (OpenGL) [Software]. http://CRAN.R-project.org/package=rgl (R package version 0.96.0)
Adler, J. (2012). R in a Nutshell (2. Aufl.). Sebastopol, CA: O’Reilly.
Google Scholar
Agresti, A. (2007). An introduction to categorical data analysis (2. Aufl.). New York, NY: Wiley.
Book MATH Google Scholar
Aiken, L. S., & West, S. G. (1991). Multiple regression: Testing and interpreting interactions. Thousand Oaks, CA: SAGE.
Google Scholar
Allaire, J. J., Cheng, J., Xie, Y., McPherson, J., Chang, W., Allen, J., $\ldots$ Hyndman, R. (2016). rmarkdown: Dynamic documents for R [Software]. http://rmarkdown.rstudio.com (R package version 1.1)
Allignol, A., & Latouche, A. (2016). CRAN task view: Survival analysis. http://CRAN.R-project.org/view=Survival (Version 2016-08-19)
Allison, P. D. (2008). Convergence failures in logistic regression. SAS Global Forum, 360, 1–11.
Google Scholar
Andres, J. (1996). Das allgemeine lineare Modell. In E. Erdfelder, R. Mausfeld, T. Meiser, & G. Rudinger (Hrsg.), Handbuch Quantitative Methoden (S. 185–200). Weinheim: Beltz.
Google Scholar
Arnold, J. B. (2016). ggthemes: Extra themes, scales and geoms for ‘ggplot2’ [Software]. https://CRAN.R-project.org/package=ggthemes (R package version 3.2.0)
Backhaus, K., Erichson, B., Plinke, W., & Weiber, R. (2015a). Multivariate Analysemethoden (14. Aufl.). Heidelberg: Springer.
MATH Google Scholar
Backhaus, K., Erichson, B., & Weiber, R. (2015b). Fortgeschrittene Multivariate Analysemethoden (3. Aufl.). Heidelberg: Springer.
Book MATH Google Scholar
Balasubramanian, N., & Johnson, S. G. (2013). cubature: Adaptive multivariate integration over hypercubes [Software]. http://CRAN.R-project.org/package=cubature (R package version 1.1-2)
Bates, D. (2004). Least squares calculations in R. R News, 4(1), 17–20. http://CRAN.R-project.org/doc/Rnews/
MathSciNet Google Scholar
Bates, D., & Maechler, M. (2016). Matrix: Sparse and Dense Matrix Classes and Methods [Software]. http://CRAN.R-project.org/package=Matrix (R package version 1.2-7.1)
Bates, D., Maechler, M., Bolker, B., & Walker, S. (2015). Fitting linear mixed-effects models using lme4. Journal of Statistical Software, 67(1), 1–48. http://www.jstatsoft.org/article/view/v067i01/
Article Google Scholar
Becker, R. A., Chambers, J. M., & Wilks, A. R. (1988). The new S language: A programming environment for data analysis and graphics. Pacific Grove, CA: Wadsworth & Brooks/Cole.
MATH Google Scholar
Bender, R., Augustin, T., & Blettner, M. (2005). Generating survival times to simulate Cox proportional hazards models. Statistics in Medicine, 24, 1713–1723.
Article MathSciNet Google Scholar
Bernaards, C. A., & Jennrich, R. I. (2005). Gradient projection algorithms and software for arbitrary rotation criteria in factor analysis. Educational and Psychological Measurement, 65(5), 676–696. http://www.stat.ucla.edu/research/gpa/
Article MathSciNet Google Scholar
Beyersmann, J., Allignol, A., & Schumacher, M. (2012). Competing risks and multistate models with R. New York, NY: Springer.
Book MATH Google Scholar
Bivand, R. S., Pebesma, E., & Gómez-Rubio, V. (2013). Applied spatial data analysis with R. New York, NY: Springer.
Book MATH Google Scholar
Bliese, P. (2016). multilevel: Multilevel Functions [Software]. http://CRAN.R-project.org/package=multilevel (R package version 2.6)
Bloomfield, V. A. (2014). Using R for numerical analysis in science and engineering. Boca Raton, FL: Chapman & Hall/CRC.
MATH Google Scholar
Boker, S., Neale, M., Maes, H., Wilde, M., Spiegel, M., Brick, T., $\ldots$ Fox, J. (2011). OpenMx: An open source extended structural equation modeling framework. Psychometrika, 66(2), 306–317. http://openmx.psyc.virginia.edu/
Borchers, H. W. (2016a). CRAN task view: Numerical mathematics. http://CRAN.R-project.org/view=NumericalMathematics (Version 2016-02-09)
Borchers, H. W. (2016b). pracma: Practical Numerical Math Functions [Software]. http://CRAN.R-project.org/package=pracma (R package version 1.9.5)
Borg, A., & Sariyar, M. (2016). RecordLinkage: Record linkage in R [Software]. https://CRAN.R-project.org/package=RecordLinkage (R package version 0.4-10)
Bortz, J., Lienert, G. A., & Boehnke, K. (2010). Verteilungsfreie Methoden in der Biostatistik (3. Aufl.). Heidelberg: Springer.
Google Scholar
Bronstein, I. N., & Semendjajew, K. A. (2012). Springer Taschenbuch der Mathematik (3. Aufl.). Berlin: Springer.
MATH Google Scholar
Buerkner, P.-C. (2016). brms: Bayesian regression models using Stan [Software]. https://CRAN.R-project.org/package=brms (R package version 1.0.1)
Büning, H., & Trenkler, G. (1994). Nichtparametrische statistische Methoden (2. Aufl.). Berlin: Walter de Gruyter.
Book MATH Google Scholar
Canty, A., & Ripley, B. D. (2016). boot: Bootstrap R (S-Plus) Functions [Software]. http://CRAN.R-project.org/package=boot (R package version 1.3-18)
Carr, D., Lewin-Koh, N., & Maechler, M. (2015). hexbin: Hexagonal Binning Routines [Software]. http://CRAN.R-project.org/package=hexbin (R package version 1.27.1)
Chambers, J. M. (2008). Software for data analysis: Programming with R. New York, NY: Springer. http://statweb.stanford.edu/~jmc4/Rbook/
Book MATH Google Scholar
Champely, S., & De Rosario, H. (2016). pwr: Basic functions for power analysis [Software]. http://CRAN.R-project.org/package=pwr (R package version 1.2-0)
Chang, W. (2013). R graphics cookbook. Sebastopol, CA: O’Reilly. http://www.cookbook-r.com/
Google Scholar
Chang, W., Cheng, J., Allaire, J. J., Xie, Y., & McPherson, J. (2016). shiny: Web application framework for R [Software]. http://CRAN.R-project.org/package=shiny (R package version 0.14.2)
Chang, W., & Luraschi, J. (2016). profvis: Interactive visualizations for profiling R code [Software]. https://rstudio.github.io/profvis/ (R package version 0.3.2)
Chernik, M. R., & LaBudde, R. A. (2011). An introduction to bootstrap methods with applications to R. Hoboken, NJ: Wiley.
Google Scholar
Chihara, L., & Hesterberg, T. (2011). Mathematical statistics with resampling and R. Hoboken, NJ: Wiley. https://sites.google.com/site/chiharahesterberg/
MATH Google Scholar
Circle Systems. (2015). Stat/Transfer [Software]. http://www.stattransfer.com/ (Version 13)
Cowlishaw, M. F. (2008). Decimal arithmetic FAQ Part 1 – General questions. http://speleotrove.com/decimal/decifaq1.html
Google Scholar
Dalgaard, P. (2007). New functions for multivariate analysis. R News, 7(2), 2–7. http://CRAN.R-project.org/doc/Rnews/
Google Scholar
Dalgaard, P. (2008). Introductory statistics with R (2. Aufl.). London, UK: Springer. http://publicifsv.sund.ku.dk/~pd/ISwR.html
Book MATH Google Scholar
Davison, A. C., & Hinkley, D. V. (1997). Bootstrap methods and their applications. Cambridge, UK: Cambridge University Press.
Book MATH Google Scholar
De Rosario-Martinez, H. (2015). phia: Post-hoc interaction analysis [Software]. http://CRAN.R-project.org/package=phia (R package version 0.2-1)
Delignette-Muller, M. L., & Dutang, C. (2015). fitdistrplus: An R package for fitting distributions. Journal of Statistical Software, 64(4), 1–34. http://www.jstatsoft.org/v64/i04/
Dowle, M., Short, T., Lianoglou, S., & Srinivasan, A. (2015). data.table: Extension of data.frame [Software]. http://CRAN.R-project.org/package=data.table (R package version 1.9.6)
Duchateau, L., & Janssen, P. (2007). The frailty model. New York, NY: Springer.
MATH Google Scholar
Dutang, C. (2016). CRAN task view: Probability distributions. http://CRAN.R-project.org/view=Distributions (Version 2016-08-06)
Eddelbuettel, D. (2013). Seamless R and C++ integration with Rcpp. New York, NY: Springer.
Book MATH Google Scholar
Eddelbuettel, D. (2016). CRAN task view: High-performance and parallel computing with R. http://CRAN.R-project.org/view=HighPerformanceComputing (Version 2016-08-18)
Eid, M., Gollwitzer, M., & Schmitt, M. (2015). Statistik und Forschungsmethoden (5. Aufl.). Weinheim: Beltz.
Google Scholar
Faraway, J. J. (2014). Linear models with R (2. Aufl.). Boca Raton, FL: Chapman & Hall/CRC. http://www.maths.bath.ac.uk/~jjf23/LMR/
MATH Google Scholar
Faraway, J. J. (2016). Extending the linear model with R: generalized linear, mixed effects and nonparametric regression models (2. Aufl.). Boca Raton, FL: Chapman & Hall/CRC. http://www.maths.bath.ac.uk/~jjf23/ELM/
MATH Google Scholar
Faul, F., Erdfelder, E., Lang, A.-G., & Buchner, A. (2007). G*Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behavior Research Methods, 39(2), 175–191. http://www.gpower.hhu.de/
Article Google Scholar
Filzmoser, P., Fritz, H., & Kalcher, K. (2016). pcaPP: Robust PCA by Projection Pursuit [Software]. http://CRAN.R-project.org/package=pcaPP (R package version 1.9-61)
Filzmoser, P., & Gschwandtner, M. (2015). mvoutlier: Multivariate outlier detection based on robust methods [Software]. http://CRAN.R-project.org/package=mvoutlier (R package version 2.0.6)
Fischer, G. (2013). Lineare algebra (18. Aufl.). Wiesbaden: Vieweg+Teubner.
Google Scholar
Fox, J. (2003). Effect displays in R for generalised linear models. Journal of Statistical Software, 8(15), 1–27. http://www.jstatsoft.org/v08/i15/
Article Google Scholar
Fox, J. (2005). The R commander: A basic-statistics graphical user interface to R. Journal of Statistical Software, 14(9), 1–42. http://www.jstatsoft.org/v14/i09/
Article Google Scholar
Fox, J. (2016a). CRAN task view: Statistics for the social sciences. http://CRAN.R-project.org/view=SocialSciences (Version 2016-08-18)
Fox, J. (2016b). polycor: Polychoric and Polyserial Correlations [Software]. http://CRAN.R-project.org/package=polycor (R package version 0.7-9)
Fox, J., Friendly, M., & Weisberg, S. (2013). Hypothesis tests for multivariate linear models using the car package. The R Journal, 5(1), 39–52. https://journal.r-project.org/archive/2013-1/
Google Scholar
Fox, J., Nie, Z., & Byrnes, J. (2016). sem: Structural Equation Models [Software]. http://CRAN.R-project.org/package=sem (R package version 3.1-8)
Fox, J., & Weisberg, S. (2011). An R companion to applied regression (2. Aufl.). Thousand Oaks, CA: SAGE. http://socserv.socsci.mcmaster.ca/jfox/Books/Companion/
Google Scholar
Fox, J., & Weisberg, S. (2016). car: Companion to Applied Regression [Software]. http://CRAN.R-project.org/package=car (R package version 2.1-3)
Friedman, J., Hastie, T., & Tibshirani, R. (2010). Regularization paths for generalized linear models via coordinate descent. Journal of Statistical Software, 33(1), 1–22. http://www.jstatsoft.org/v33/i01/
Article Google Scholar
Gabry, J., & Goodrich, B. (2016). rstanarm: Bayesian applied regression modeling via Stan [Software]. https://CRAN.R-project.org/package=rstanarm (R package version 2.12.1)
Gałecki, A. T., & Burzykowski, T. (2013). Linear mixed-effects models using R: A step-by-step approach. New York, NY: Springer.
Book MATH Google Scholar
Gandrud, C. (2015). Reproducible research with R & RStudio (2. Aufl.). Boca Raton, FL: Chapman & Hall/CRC. http://christophergandrud.github.io/RepResR-RStudio/
MATH Google Scholar
Garnier, S. (2016). viridis: Default color maps from ‘matplotlib’ [Software]. https://CRAN.R-project.org/package=viridis (R package version 0.3.4)
Gentleman, R. C., Carey, V. J., Bates, D. M., Bolstad, B., Dettling, M., Dudoit, S., $\ldots$ Zhang, J. (2004). Bioconductor: Open software development for computational biology and bioinformatics. Genome Biology, 5, R80. http://genomebiology.com/2004/5/10/R80
Genz, A., & Bretz, F. (2009). Computation of multivariate normal and t probabilities (Lecture notes in statistics, Vol. 195). Heidelberg: Springer.
Book MATH Google Scholar
Genz, A., Bretz, F., Miwa, T., Mi, X., Leisch, F., Scheipl, F., & Hothorn, T. (2016). mvtnorm: Multivariate normal and t distributions [Software]. http://CRAN.R-project.org/package=mvtnorm (R package version 1.0-5)
Gesmann, M., & de Castillo, D. (2011). googleVis: Interface between R and the Google Visualisation API. The R Journal, 3(2), 40–44. http://journal.r-project.org/archive/2011-2/
Google Scholar
Gilbert, P., & Varadhan, R. (2016). numDeriv: Accurate numerical derivatives [Software]. https://CRAN.R-project.org/package=numDeriv (R package version 2016.8-1)
Goldberg, D. (1991). What every computer scientist should know about floating-point arithmetic. ACM Computing Surveys, 23(1), 5–48. http://www.validlab.com/goldberg/paper.pdf
Article MathSciNet Google Scholar
Good, P. I. (2004). Permutation, parametric, and bootstrap tests of hypotheses (3. Aufl.). New York, NY: Springer.
MATH Google Scholar
Goulet, V., Dutang, C., Maechler, M., Firth, D., Shapira, M., & Stadelmann, M. (2015). expm: Matrix exponential [Software]. http://CRAN.R-project.org/package=expm (R package version 0.999-0)
Goyvaerts, J., & Levithan, S. (2012). Regular expressions cookbook. Sebastopol, CA: O’Reilly. http://www.regular-expressions.info/
MATH Google Scholar
Grolemund, G., & Wickham, H. (2011). Dates and Times Made Easy with lubridate. Journal of Statistical Software, 40(3), 1–25. http://www.jstatsoft.org/v40/i03/
Article Google Scholar
Grothendieck, G., & Petzoldt, T. (2004). Date and Time Classes in R. R News, 4(1), 29–32. http://CRAN.R-project.org/doc/Rnews/
Google Scholar
Harrell Jr, F. E. (2015). Regression modeling strategies (2. Aufl.). New York: Springer. http://biostat.mc.vanderbilt.edu/wiki/Main/RmS
Book MATH Google Scholar
Harrell Jr, F. E. (2016a). Hmisc: Harrell Miscellaneous [Software]. http://CRAN.R-project.org/package=Hmisc (R package version 4.0-0)
Harrell Jr, F. E. (2016b). rms: Regression Modeling Strategies [Software]. http://CRAN.R-project.org/package=rms (R package version 5.0-0)
Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning: data mining, inference, and prediction (2. Aufl.). New York: Springer. http://statweb.stanford.edu/~tibs/ElemStatLearn/
Book MATH Google Scholar
Heiberger, R. M., & Neuwirth, E. (2009). R through Excel. New York, NY: Springer.
Book MATH Google Scholar
Heinze, G., Ploner, M., Dunkler, D., & Southworth, H. (2013). logistf: Firth’s bias reduced logistic regression [Software]. http://CRAN.R-project.org/package=logistf (R package version 1.21)
Hendrickx, J. (2012). perturb: Tools for evaluating collinearity [Software]. http://CRAN.R-project.org/package=perturb (R package version 2.05)
Hesterberg, T. (2015). resample: Resampling functions [Software]. http://CRAN.R-project.org/package=resample (R package version 0.4)
Hewson, P. (2015). CRAN task view: Multivariate statistics. http://CRAN.R-project.org/view=Multivariate (Version 2015-07-01)
Hoffman, D. D. (2000). Visual intelligence: How we create what we see. New York, NY: W. W. Norton & Company.
Google Scholar
Højsgaard, S., Halekoh, U., & Yan, J. (2006). The R package geepack for generalized estimating equations. Journal of Statistical Software, 15(2), 1–11. http://www.jstatsoft.org/v15/i02/
Google Scholar
Honaker, J., King, G., & Blackwell, M. (2011). Amelia: II: A program for missing data. Journal of Statistical Software, 45(7), 1–47. http://www.jstatsoft.org/v45/i07/
Article Google Scholar
Hornik, K. (2016). The R FAQ [Software-Handbuch]. http://CRAN.R-project.org/doc/FAQ/
Google Scholar
Hosmer Jr, D. W., Lemeshow, S., & May, S. (2008). Applied survival analysis: Regression modeling of time to event data (2. Aufl.). New York, NY: Wiley.
Book MATH Google Scholar
Hothorn, T. (2016). CRAN task view: Machine learning & statistical learning. http://CRAN.R-project.org/view=MachineLearning (Version 2016-06-24)
Hothorn, T., Bretz, F., & Westfall, P. (2008). Simultaneous inference in general parametric models. Biometrical Journal, 50(3), 346–363.
Article MathSciNet MATH Google Scholar
Hothorn, T., & Everitt, B. S. (2014). A handbook of statistical analysis using R (3. Aufl.). Boca Raton, FL: Chapman & Hall/CRC.
MATH Google Scholar
Hothorn, T., & Everitt, B. S. (2015). HSAUR3: A handbook of statistical analyses using R (3rd ed.) [Software]. http://CRAN.R-project.org/package=HSAUR3 (R package version 1.0-5)
Hothorn, T., Hornik, K., van de Wiel, M. A., & Zeileis, A. (2008). Implementing a class of permutation tests: The coin package. Journal of Statistical Software, 28(8), 1–23. http://www.jstatsoft.org/v28/i08/
Article Google Scholar
Huber, W., Carey, V. J., Gentleman, R., Anders, S., Carlson, M., Carvalho, B. S., $\ldots$ Morgan, M. (2015). Orchestrating high-throughput genomic analysis with Bioconductor. Nature Methods, 12, 115–121.
Google Scholar
Hyndman, R. J. (2016). CRAN task view: Time series analysis. http://CRAN.R-project.org/view=TimeSeries (Version 2016-09-09)
Ihaka, R., & Gentleman, R. (1996). R: A language for data analysis and graphics. Journal of Computational and Graphical Statistics, 5(3), 299–314.
Google Scholar
Ihaka, R., Murrell, P., Fisher, J. C., & Zeileis, A. (2015). colorspace: Color Space Manipulation [Software]. http://CRAN.R-project.org/package=colorspace (R package version 1.2-6)
James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning with applications in R. New York, NY: Springer. http://www-bcf.usc.edu/~gareth/ISL/
Book MATH Google Scholar
Johnson, P. E. (2016). rockchalk: Regression Estimation and Presentation [Software]. http://CRAN.R-project.org/package=rockchalk (R package version 1.8.101)
Johnson, S. G. (2014). The NLopt nonlinear-optimization package [Software]. http://ab-initio.mit.edu/nlopt/ (Version 2.4.2)
Kelley, K. (2016). MBESS [Software]. http://CRAN.R-project.org/package=MBESS (R package version 4.1.0)
Kirk, R. E. (2013). Experimental design – Procedures for the social sciences (4. Aufl.). Thousand Oaks, CA: SAGE. http://www.sagepub.com/kirk/
Book Google Scholar
Klein, J. P., & Moeschberger, M. L. (2003). Survival analysis: Techniques for censored and truncated data (2. Aufl.). New York, NY: Springer.
MATH Google Scholar
Kleinman, K., & Horton, N. J. (2014). SAS and R: Data management, statistical analysis, and graphics (2. Aufl.). Boca Raton, FL: Chapman Hall/CRC. http://nhorton.people.amherst.edu/sasr2/
Book MATH Google Scholar
Kolde, R. (2015). pheatmap: Pretty heatmaps [Software]. https://CRAN.R-project.org/package=pheatmap (R package version 1.0.8)
Kruschke, J. K. (2015). Doing Bayesian data analysis: A tutorial with R, JAGS, and Stan (2. Aufl.). Amsterdam: Academic Press.
MATH Google Scholar
Kuhn, M. (2016). caret: Classification and regression training [Software]. http://CRAN.R-project.org/package=caret (R package version 6.0-71)
Kuhn, M., & Johnson, K. (2013). Applied predictive modeling. New York, NY: Springer. http://appliedpredictivemodeling.com/
Book MATH Google Scholar
Leeper, T., Chamberlain, S., Mair, P., Ram, K., & Gandrud, C. (2016). CRAN task view: Web technologies and services. http://CRAN.R-project.org/view=WebTechnologies (Version 2016-08-18)
Leisch, F., & Gruen, B. (2016). CRAN task view: Cluster analysis & finite mixture models. http://CRAN.R-project.org/view=Cluster (Version 2016-08-18)
Lemon, J. (2006). Plotrix: a package in the red light district of R. R News, 6(4), 8–12.
Google Scholar
Ligges, U. (2002). R Help Desk: Automation of mathematical annotation in plots. R News, 2(3), 32–34. http://CRAN.R-project.org/doc/Rnews/
Google Scholar
Ligges, U. (2003). R Help Desk: Package management. R News, 3(3), 37–39. http://CRAN.R-project.org/doc/Rnews/
Google Scholar
Ligges, U. (2016). Programmieren mit R (4. Aufl.). Berlin: Springer Spektrum.
MATH Google Scholar
Ligges, U., & Fox, J. (2008). R Help Desk: How can I avoid this loop or make it faster? R News, 8(1), 46–50. http://CRAN.R-project.org/doc/Rnews/
Google Scholar
Long, J. D. (2012). Longitudinal data analysis for the behavioral sciences using R. Thousand Oaks, CA: SAGE. https://studysites.sagepub.com/long/
Google Scholar
Lumley, T. (2009). leaps: Regression subset selection (using Fortran code by Alan Miller) [Software]. http://CRAN.R-project.org/package=leaps (R package version 2.9)
Maechler, M. (2016). CRAN task view: Robust statistical methods. http://CRAN.R-project.org/view=Robust (Version 2016-08-29)
Maindonald, J., & Braun, W. J. (2010). Data analysis and graphics using R: An example-based approach (3. Aufl.). Cambridge, UK: Cambridge University Press. http://maths-people.anu.edu.au/~johnm/r-book/daagur3.html
Book MATH Google Scholar
Maindonald, J., & Braun, W. J. (2015). DAAG: Data Analysis And Graphics data and functions [Software]. http://CRAN.R-project.org/package=DAAG (R package version 1.22)
Mair, P. (2016). CRAN task view: Psychometric models and methods. http://CRAN.R-project.org/view=Psychometrics (Version 2016-08-18)
Mardia, K. V., Kent, J. T., & Bibby, J. M. (1980). Multivariate Analysis. London, UK: Academic Press.
MATH Google Scholar
Maronna, R. A., Martin, R. D., & Yohai, V. J. (2006). Robust Statistics: Theory and Methods. New York, NY: Wiley.
Book MATH Google Scholar
Maxwell, S. E., & Delaney, H. D. (2004). Designing experiments and analyzing data: A model comparison perspective (2. Aufl.). Mahwah, NJ: Lawrence Erlbaum.
MATH Google Scholar
Mazerolle, M. J. (2016). AICcmodavg: Model selection and multimodel inference based on (Q)AIC(c) [Software]. http://CRAN.R-project.org/package=AICcmodavg (R package version 2.0-4)
McElreath, R. (2015). Statistical rethinking: A Bayesian course with examples in R and Stan. Boca Raton, FL: Chapman & Hall/CRC.
Google Scholar
McFarlane, J. (2016). pandoc: A universal document converter [Software]. http://pandoc.org/ (Version 1.18)
Mersmann, O. (2015). microbenchmark: Sub microsecond accurate timing functions [Software]. http://CRAN.R-project.org/package=microbenchmark (R package version 1.4-2.1)
Meyer, D., & Hornik, K. (2009). Generalized and customizable sets in R. Journal of Statistical Software, 31(2), 1–27. http://www.jstatsoft.org/v31/i02/
Article Google Scholar
Meyer, D., Zeileis, A., & Hornik, K. (2016). vcd: Visualizing Categorical Data [Software]. http://CRAN.R-project.org/package=vcd (R package version 1.4-3)
Meyer, S., & Held, L. (2014). Power-law models for infectious disease spread. The Annals of Applied Statistics, 8(3), 1612–1639.
Article MathSciNet MATH Google Scholar
Microsoft. (2016). checkpoint: Install packages from snapshots on the checkpoint server for producibility [Software]. https://CRAN.R-project.org/package=checkpoint (R package version 0.3.18)
Microsoft. (2016a). Microsoft R Open [Software]. http://mran.revolutionanalytics.com/open/ (3.3.1)
Microsoft. (2016b). R Tools for Visual Studio [Software]. https://www.visualstudio.com/vs/rtvs/ (Version 0.5)
Miller, A. J. (2002). Subset selection in regression (2. Aufl.). Boca Raton, FL: Chapman & Hall/CRC.
Book MATH Google Scholar
Muenchen, R. A. (2011). R for SAS and SPSS users (2. Aufl.). New York, NY: Springer. http://r4stats.com/
Book MATH Google Scholar
Muenchen, R. A., & Hilbe, J. M. (2010). R for Stata users. New York, NY: Springer. http://r4stats.com/
Book MATH Google Scholar
Murrell, P. (2011). R Graphics (2. Aufl.). Boca Raton, FL: Chapman & Hall/CRC. http://www.stat.auckland.ac.nz/~paul/RG2e/
MATH Google Scholar
Nash, J. C. (2014a). Nonlinear parameter optimization using R tools. Chichester, UK: Wiley.
Book MATH Google Scholar
Nash, J. C. (2014b). On best practice optimization methods in R. Journal of Statistical Software, 60(2), 1–14. http://www.jstatsoft.org/v60/i02/
Article Google Scholar
Nash, J. C., & Ravi, V. (2011). Unifying optimization algorithms to aid software system users: Optimx for R. Journal of Statistical Software, 43(9), 1–14. http://www.jstatsoft.org/v43/i09/
Article Google Scholar
Neuwirth, E. (2014). RColorBrewer: ColorBrewer palettes [Software]. http://CRAN.R-project.org/package=RColorBrewer (Version 1.1-2)
Neuwirth, E. (2016). RExcel [Software]. http://rcom.univie.ac.at/ (Version 3.2.16)
Oksanen, J., Blanchet, F. G., Kindt, R., Legendre, P., Minchin, P. R., O’Hara, R. B., $\ldots$ Wagner, H. (2016). vegan: Community Ecology Package [Software]. http://CRAN.R-project.org/package=vegan (R package version 2.4-1)
OpenAnalytics BVBA. (2015). Architect [Software]. http://www.openanalytics.eu/architect/ (Version 0.9.8)
Park, J. H. (2016). CRAN task view: Bayesian inference. http://CRAN.R-project.org/view=Bayesian (Version 2016-09-19)
Pau, G., Fuchs, F., Sklyar, O., Boutros, M., & Huber, W. (2010). EBImage – an R package for image processing with applications to cellular phenotypes. Bioinformatics, 26(7), 979–981. http://www.bioconductor.org/packages/devel/bioc/html/EBImage.html
Article Google Scholar
Peters, A., & Hothorn, T. (2015). ipred: Improved predictors [Software]. http://CRAN.R-project.org/package=ipred (R package version 0.9-5)
Pinheiro, J. C., & Bates, D. M. (2000). Mixed-effects models in S and S-PLUS. New York, NY: Springer.
Book MATH Google Scholar
Pinheiro, J. C., Bates, D. M., DebRoy, S., Sarkar, D., & R Core Team. (2016). nlme: Linear and Nonlinear Mixed Effects Models [Software]. http://CRAN.R-project.org/package=nlme (R package version 3.1-128)
Plate, T., & Heiberger, R. (2016). abind: Combine multi-dimensional arrays [Software]. http://CRAN.R-project.org/package=abind (R package version 1.4-5)
Ploner, M., & Heinze, G. (2015). coxphf: Cox regression with Firth’s penalized likelihood [Software]. http://CRAN.R-project.org/package=coxphf (R package version 1.11)
Polzehl, J., & Tabelow, K. (2007). Adaptive Smoothing of Digital images: The R Package adimpro. Journal of Statistical Software, 19(1), 1–17. http://www.jstatsoft.org/v19/i01/
Article Google Scholar
Poncet, P. (2012). modeest: Mode estimation [Software]. http://CRAN.R-project.org/package=modeest (R package version 2.1)
R Core Team. (2014). R: A language and environment for statistical computing [Software-Handbuch]. Vienna, Austria. http://www.r-project.org/
Google Scholar
R Core Team. (2016a). foreign: Read data stored by Minitab, S, SAS, SPSS, Stata, Systat, dBase, …[Software]. http://CRAN.R-project.org/package=foreign (R package version 0.8-67)
R Core Team. (2016b). R: Data Import/Export [Software-Handbuch]. Vienna, Austria. http://CRAN.R-project.org/doc/manuals/R-data.html
Google Scholar
R Core Team. (2016c). R: Installation and Administration [Software-Handbuch]. Vienna, Austria. http://CRAN.R-project.org/doc/manuals/R-admin.html
Google Scholar
R Core Team. (2016d). Writing R Extensions [Software-Handbuch]. Vienna. http://cran.r-project.org/doc/manuals/R-exts.html
R Foundation for Statistical Computing. (2014). R: Regulatory compliance and validation issues – A guidance document for the use of R in regulated clinical trial environments [Software-Handbuch]. Vienna, Austria. http://www.r-project.org/doc/R-FDA.pdf
R Special Interest Group on Databases, Wickham, H., & Müller, K. (2016). DBI: R database interface [Software]. http://CRAN.R-project.org/package=DBI (R package version 0.5-1)
Revelle, W. (2016). psych: Procedures for Psychological, Psychometric, and Personality Research [Software]. http://CRAN.R-project.org/package=psych (R package version 1.6.9)
Ripley, B. D. (2016). RODBC: ODBC Database Access [Software]. http://CRAN.R-project.org/package=RODBC (R package version 1.3-14)
Ripley, B. D., & Murdoch, D. (2016). R for Windows FAQ. http://CRAN.R-project.org/bin/windows/base/rw-FAQ.html
Google Scholar
Ritz, C., & Streibig, J. C. (2009). Nonlinear regression with R. New York, NY: Springer.
Book MATH Google Scholar
Robin, X., Turck, N., Hainard, A., Tiberti, N., Lisacek, F., Sanchez, J.-C., & Müller, M. (2011). pROC: an open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinformatics, 12, 77. http://web.expasy.org/pROC/
Article Google Scholar
Rödiger, S., Friedrichsmeier, T., Prasenjit, K., & Michalke, M. (2011). RKWard: A Comprehensive Graphical User Interface and Integrated Development Environment for Statistical Analysis with R. Journal of Statistical Software, 49(9), 1–34. http://www.jstatsoft.org/v49/i09/
Google Scholar
Rosseel, Y. (2012). lavaan: An R Package for Structural Equation Modeling. Journal of Statistical Software, 48(2), 1–36. http://www.jstatsoft.org/v48/i02/
Article Google Scholar
Rossini, A. J., Maechler, M., Sparapani, R. A., Eglen, S., Spinu, V., & Heiberger, R. M. (2016). ESS: Emacs Speaks Statistics [Software]. http://ess.r-project.org/ (Version 16.04)
Google Scholar
Rousseeuw, P. J., Croux, C., Todorov, V., Ruckstuhl, A., Salibian-Barrera, M., Verbeke, T., & Maechler, M. (2016). robustbase: Basic Robust Statistics [Software]. http://CRAN.R-project.org/package=robustbase (R package version 0.92-6)
Rousseeuw, P. J., & van Zomeren, B. C. (1990). Unmasking Multivariate Outliers and Leverage Points. Journal of the American Statistical Association, 85(411), 633–639. http://www.jstor.org/stable/2289995
Article Google Scholar
RStudio Inc. (2016a). ggvis: Interactive grammar of graphics [Software]. http://CRAN.R-project.org/package=ggvis (R package version 0.4.3)
RStudio Inc. (2016b). RStudio [Software]. http://www.rstudio.org/ (Version 1.0.39)
Shumway, R. H., & Stoffer, D. S. (2016). Time Series Analysis and Its Applications (4. Aufl.). New York, NY: Springer. http://www.stat.pitt.edu/stoffer/tsa4/
MATH Google Scholar
Sievert, C., Parmer, C., Hocking, T., Chamberlain, S., Ram, K., Corvellec, M., & Despouy, P. (2016). plotly: Create interactive web graphics via Plotly’s JavaScript graphing library [Software]. https://CRAN.R-project.org/package=plotly (R package version 4.5.2)
Signorell, A. (2016). DescTools: Tools for descriptive statistics. http://CRAN.R-project.org/package=DescTools (R package version 0.99.17)
Smyth, G., Hu, Y., Dunn, P., Phipson, B., & Chen, Y. (2016). statmod: Statistical modeling [Software]. https://CRAN.R-project.org/package=statmod (R package version 1.4.26)
Soetaert, K. (2015). rootSolve: Nonlinear root finding, equilibrium and steady-state analysis of ordinary differential equations [Software]. https://CRAN.R-project.org/package=rootSolve (R package version 1.6.6)
Soetaert, K., & Petzoldt, T. (2015). CRAN task view: Differential equations. http://CRAN.R-project.org/view=DifferentialEquations (Version 2015-07-03)
Spector, P. (2008). Data manipulation with R. New York, NY: Springer.
MATH Google Scholar
Strang, G. (2003). Lineare Algebra. Berlin: Springer.
Book MATH Google Scholar
Su, Y.-S., Gelman, A., Hill, J., & Yajima, M. (2011). Multiple imputation with diagnostics (mi) in R: Opening windows into the black box. Journal of Statistical Software, 45(2), 1–67. http://www.jstatsoft.org/v45/i02/
Article Google Scholar
Templ, M. (2016). CRAN task view: Official statistics & survey methodology. http://CRAN.R-project.org/view=OfficialStatistics (Version 2016-06-28)
Therneau, T. (2016). survival: Survival analysis [Software]. http://CRAN.R-project.org/package=survival (R package version 2.39-5)
Theussl, S., & Borchers, H. W. (2016). CRAN task view: Optimization and mathematical programming. http://CRAN.R-project.org/view=Optimization (Version 2016-06-06)
TIBCO Software Inc. (2016). TIBCO enterprise runtime for R (TERR) [Software]. http://spotfire.tibco.com/ (Version 4.1)
Tingley, D., Yamamoto, T., Keele, L., & Imai, K. (2015). mediation: R Package for Causal Mediation Analysis [Software]. http://CRAN.R-project.org/package=mediation (R package version 4.4.5)
Unwin, A. (2015). Graphical data analysis with R. Boca Raton, FL: Chapman & Hall/CRC. http://www.gradaanwr.net/
MATH Google Scholar
Urbanek, S., & Horner, J. (2015). Cairo: R graphics device using cairo graphics library for creating high-quality bitmap (PNG, JPEG, TIFF), vector (PDF, SVG, PostScript) and display (X11 and Win32) output [Software]. http://CRAN.R-project.org/package=Cairo (R package version 1.5-9)
Ushey, K., McPherson, J., Cheng, J., Atkins, A., & Allaire, J. (2016). packrat: A dependency management system for projects and their R package pendencies [Software]. https://CRAN.R-project.org/package=packrat (R package version 0.4.8-1)
van Buuren, S. (2012). Flexible imputation of missing data. Boca Raton, FL: Chapman & Hall/CRC.
Book MATH Google Scholar
van Buuren, S., & Groothuis-Oudshoorn, K. (2011). MICE: Multivariate Imputation by Chained Equations in R. Journal of Statistical Software, 45(3), 1–67. http://www.jstatsoft.org/v45/i03/
Article Google Scholar
van der Loo, M. P. J. (2014). The stringdist package for approximate string matching. The R Journal, 6,111–122. http://CRAN.R-project.org/package=stringdist
Google Scholar
Venables, W. N. (2016). codingMatrices: Alternative factor coding matrices for linear model formulae [Software]. https://CRAN.R-project.org/package=codingMatrices (R package version 0.2.1)
Venables, W. N., & Ripley, B. D. (2002). Modern applied statistics with S (4. Aufl.). New York, NY: Springer. http://www.stats.ox.ac.uk/pub/MASS4/
Book MATH Google Scholar
Venables, W. N., Smith, D. M., Gentleman, R., Ihaka, R., Maechler, M., & R Core Team. (2016). An introduction to R [Software-Handbuch]. Vienna. http://CRAN.R-project.org/doc/manuals/R-intro.html
Wahlbrink, S. (2016). Eclipse Plug-In for R: StatET [Software]. http://www.walware.de/goto/statet (Version 3.5.1)
Walker, A. (2015). openxlsx: Read, write and edit XLSX files [Software]. http://CRAN.R-project.org/package=openxlsx (R package version 3.0.0)
Wand, M. (2015). KernSmooth: Functions for kernel smoothing for Wand & Jones (1995) [Software]. http://CRAN.R-project.org/package=KernSmooth (R package version 2.23-15j)
West, B. T., Welch, K. B., & Gałecki, A. T. (2014). Linear mixed models: A Practical guide using statistical software (2. Aufl.). Boca Raton, FL: Chapman & Hall/CRC. http://www-personal.umich.edu/~agalecki/
MATH Google Scholar
Wickham, H. (2014). Advanced R programming. Boca Raton, FL: Chapman & Hall/CRC. http://adv-r.had.co.nz/
Google Scholar
Wickham, H. (2015). R packages. Sebastopol, CA: O’Reilly. http://r-pkgs.had.co.nz/
Google Scholar
Wickham, H. (2016a). devtools: Tools to make developing R code easier [Software]. http://CRAN.R-project.org/package=devtools (R package version 1.12.0)
Wickham, H. (2016b). readxl: Read Excel files [Software]. http://CRAN.R-project.org/package=readxl (R package version 0.1.1)
Wickham, H. (2016c). stringr: Simple, consistent wrappers for common string operations [Software]. http://CRAN.R-project.org/package=stringr (R package version 1.1.0)
Wickham, H. (2016d). tidyr: Easily tidy data with spread and gather functions [Software]. http://CRAN.R-project.org/package=tidyr (R package version 0.6.0)
Wickham, H. (2016e). tidyverse: Easily install and load ‘tidyverse’ packages [Software]. https://CRAN.R-project.org/package=tidyverse (R package version 1.0.0)
Wickham, H., Cook, D., Hofmann, H., & Buja, A. (2011). tourr: An R package for exploring multivariate data with projections. Journal of Statistical Software, 40(2), 1–18. http://www.jstatsoft.org/v40/i02/
Article Google Scholar
Wickham, H., & Francois, R. (2016a). dplyr: A grammar of data manipulation [Software]. http://CRAN.R-project.org/package=dplyr (R package version 0.5.0)
Wickham, H., & Francois, R. (2016b). readr: Read tabular data [Software]. http://CRAN.R-project.org/package=readr (R package version 1.0.0)
Wickham, H., & Grolemund, G. (2016). R for data science. Sebastopol, CA: O’Reilly. http://r4ds.had.co.nz/
Google Scholar
Wickham, H., James, D. A., Falcon, S., SQLite Authors, Healy, L., & RStudio Inc. (2014). RSQLite: SQLite interface for R [Software]. http://CRAN.R-project.org/package=RSQLite (R package version 1.0.0)
Wickham, H., & Miller, E. (2016). haven: Import SPSS, Stata and SAS files [Software]. http://CRAN.R-project.org/package=haven (R package version 1.0.0)
Wickham, H., & Sievert, C. (2016). ggplot2: Elegant graphics for data analysis (2. Aufl.). New York, NY: Springer. http://github.com/hadley/ggplot2-book
Book MATH Google Scholar
Wilke, C. O. (2016). cowplot: Streamlined plot theme and plot annotations for ggplot2 [Software]. https://CRAN.R-project.org/package=cowplot (R package version 0.6.3)
Wilkinson, G. N., & Rogers, C. E. (1973). Symbolic description of factorial models for analysis of variance. Applied Statistics, 22, 392–399.
Article Google Scholar
Wilkinson, L. (2005). The Grammar of Graphics (2. Aufl.). New York, NY: Springer.
MATH Google Scholar
Wirtz, M., & Caspar, F. (2002). Beurteilerübereinstimmung und Beurteilerreliabilität. Göttingen: Hogrefe.
Google Scholar
Wood, S. N. (2006). Generalized additive models: An introduction with R. Boca Raton, FL: Chapman & Hall/CRC.
MATH Google Scholar
Würtz, D., Chalabi, Y., & Maechler, M. (2015). timeDate: Rmetrics – Chronological and Calendar Objects [Software]. http://CRAN.R-project.org/package=timeDate (R package version 3012.100)
Xie, Y. (2015). Dynamic documents with R and knitr (2. Aufl.). Boca Raton, FL: Chapman & Hall/CRC.
Google Scholar
Xie, Y. (2016). knitr: A general-purpose package for dynamic report generation in R [Software]. http://CRAN.R-project.org/package=knitr (R package version 1.14)
Yan, J., & Fine, J. P. (2004). Estimating equations for association structures. Statistics in Medicine, 23(6), 859–874.
Article Google Scholar
Yee, T. W. (2010). The VGAM package for categorical data analysis. Journal of Statistical Software, 32(10), 1–34. http://www.jstatsoft.org/v32/i10/
Article Google Scholar
Zeileis, A. (2004). Econometric computing with HC and HAC covariance matrix estimators. Journal of Statistical Software, 11(10), 1–17. http://www.jstatsoft.org/v11/i10/
Article Google Scholar
Zeileis, A. (2005). CRAN task views. R News, 5(1), 39–40. http://CRAN.R-project.org/doc/Rnews/
MathSciNet Google Scholar
Zeileis, A., & Hothorn, T. (2002). Diagnostic checking in regression relationships. R News, 2(3), 7–10. http://CRAN.R-project.org/doc/Rnews/
Google Scholar
Zeileis, A., Kleiber, C., & Jackman, S. (2008). Regression models for count data in R. Journal of Statistical Software, 27(8), 1–25. http://www.jstatsoft.org/v27/i08/
Article Google Scholar
Zelterman, D. (2015). Applied multivariate statistics with R. Cham: Springer.
Book Google Scholar

Download references

Author information

Authors and Affiliations

Institut für Medizinische Biometrie, Epidemiologie und Informatik (IMBEI), Universitätsmedizin der Johannes Gutenberg-Universität Mainz, Mainz, Deutschland
Daniel Wollschläger

Authors

Daniel Wollschläger
View author publications
You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

Copyright information

About this chapter

Cite this chapter

Wollschläger, D. (2017). Resampling-Verfahren. In: Grundlagen der Datenanalyse mit R. Statistik und ihre Anwendungen. Springer Spektrum, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-53670-4_11

Download citation

DOI: https://doi.org/10.1007/978-3-662-53670-4_11
Published: 28 March 2017
Publisher Name: Springer Spektrum, Berlin, Heidelberg
Print ISBN: 978-3-662-53669-8
Online ISBN: 978-3-662-53670-4
eBook Packages: Life Science and Basic Disciplines (German Language)

Publish with us

Policies and ethics