Beyond differences in means: robust graphical methods to compare two groups in neuroscience

European Journal of Neuroscience

Volumn 46, Issue 2, 2017, Pages 1738-1748

  Rousselet, Guillaume A ^a   Pernet, Cyril R ^b   Wilcox, Rand R ^c  

^a UNIVERSITY OF GLASGOW   (United Kingdom)

^b UNIVERSITY OF EDINBURGH   (United Kingdom)

^c UNIVERSITY OF SOUTHERN CALIFORNIA   (United States)

Author keywords

data visualisation; difference asymmetry function; quantile estimation; robust statistics; shift function

Indexed keywords

ARTICLE; BASIC RESEARCH; COMPARATIVE STUDY; COMPUTER GRAPHICS; CONTROLLED STUDY; HUMAN; INFERENTIAL STATISTICS; INFORMATION PROCESSING; NEUROSCIENCE; PRIORITY JOURNAL; SAMPLE SIZE; STATISTICAL PARAMETERS; STUDENT T TEST; ANIMAL; GUINEA PIG; MORTALITY; MYCOBACTERIOSIS; PROCEDURES; SOFTWARE; STATISTICAL ANALYSIS; TIME FACTOR;

ANIMALS; COMPUTER GRAPHICS; DATA INTERPRETATION, STATISTICAL; GUINEA PIGS; HUMANS; MYCOBACTERIUM INFECTIONS; NEUROSCIENCES; SOFTWARE; TIME FACTORS;

EID: 85021401837     PISSN: 0953816X     EISSN: 14609568     Source Type: Journal    
DOI: 10.1111/ejn.13610     Document Type: Article

References (46)

1. Allen, E.A., Erhardt, E.B. & Calhoun, V.D. (2012) Data visualization in the neurosciences: overcoming the curse of dimensionality. Neuron, 74, 603–608.
2. Anscombe, F.J. (1973) Graphs in statistical analysis. Am. Stat., 27, 17–21.
3. Bieniek, M.M., Bennett, P.J., Sekuler, A.B. & Rousselet, G.A. (2016) A robust and representative lower bound on object processing speed in humans. Eur. J. Neuorsci., 44, 1804–1814.
4. Bjerkedal, T. (1960) Acquisition of resistance in guinea pigs infected with different doses of virulent tubercle bacilli. Am. J. Hyg., 72, 130–148.
5. Button, K.S., Ioannidis, J.P., Mokrysz, C., Nosek, B.A., Flint, J., Robinson, E.S. & Munafo, M.R. (2013) Power failure: why small sample size undermines the reliability of neuroscience. Nat. Rev. Neurosci., 14, 365–376.
6. Chanauria, N., Bharmauria, V., Bachatene, L., Cattan, S., Rouat, J. & Molotchnikoff, S. (2016) Comparative effects of adaptation on layers II–III and V–VI neurons in cat V1. Eur. J. Neuorsci., 44, 3094–3104.
7. Clarke, E. & Sherrill-Mix, S. (2016) ggbeeswarm: Categorical Scatter (Violin Point) Plots. R package version 0.5.3. Available https://cran.r-project.org/package=ggbeeswarm.
8. Colquhoun, D. (2014) An investigation of the false discovery rate and the misinterpretation of p-values. R. Soc. Open Sci., 1, 140216.
9. Cook, D., Lee, E.K. & Majumder, M. (2016) Data visualization and statistical graphics in big data analysis. Annu. Rev. Stat. Appl., 3, 133–159.
10. De Jong, R., Liang, C.C. & Lauber, E. (1994) Conditional and unconditional automaticity - a dual-process model of effects of spatial stimulus - response correspondence. J. Exp. Psychol. Human, 20, 731–750.
11. Doksum, K. (1974) Empirical probability plots and statistical inference for nonlinear models in the two-sample case. Ann. Stat., 2, 267–277.
12. Doksum, K.A. (1977) Some graphical methods in statistics. A review and some extensions. Stat. Neerl., 31, 53–68.
13. Doksum, K.A. & Sievers, G.L. (1976) Plotting with confidence - graphical comparisons of 2 populations. Biometrika, 63, 421–434.
14. Duke, S.P., Bancken, F., Crowe, B., Soukup, M., Botsis, T. & Forshee, R. (2015) Seeing is believing: good graphic design principles for medical research. Stat. Med., 34, 3040–3059.
15. Erceg-Hurn, D.M. & Mirosevich, V.M. (2008) Modern robust statistical methods: an easy way to maximize the accuracy and power of your research. Am. Psychol., 63, 591–601.
16. Forstmeier, W., Wagenmakers, E.-J. & Parker, T.H. (2016) Detecting and avoiding likely false-positive findings - a practical guide. Biol. Rev. https://doi.org/10.1111/brv.12315. [Epub ahead of print.]
17. Handcock, M.S. & Morris, M. (1998) Relative distribution methods. Sociol. Methodol., 28, 53–97.
18. Harrell, F.E. & Davis, C.E. (1982) A new distribution-free quantile estimator. Biometrika, 69, 635–640.
19. Ince, R.A., Giordano, B.L., Kayser, C., Rousselet, G.A., Gross, J. & Schyns, P.G. (2017) A statistical framework for neuroimaging data analysis based on mutual information estimated via a gaussian copula. Hum. Brain Mapp., 38, 1541–1573.
20. Massidda, D. (2013) retimes: Reaction Time Analysis. R package version 0.1-2. Available https://cran.r-project.org/package=retimes.
21. Munafò, M.R., Nosek, B.A., Bishop, D.V.M., Button, K.S., Chambers, C.D., Percie du Sert, N., Simonsohn, U., Wagenmakers, E.-J. et al. (2017) A manifesto for reproducible science. Nat. Hum. Behav., 1, 0021.
22. Pernet, C., Andersson, J., Paulesu, E. & Demonet, J.F. (2009a) When all hypotheses are right: a multifocal account of dyslexia. Hum. Brain Mapp., 30, 2278–2292.
23. Pernet, C.R., Poline, J.B., Demonet, J.F. & Rousselet, G.A. (2009b) Brain classification reveals the right cerebellum as the best biomarker of dyslexia. BMC Neurosci., https://doi.org/10.1186/1471-2202-10-67 [Epub ahead of print.]
24. Poldrack, R.A., Baker, C.I., Durnez, J., Gorgolewski, K.J., Matthews, P.M., Munafo, M.R., Nichols, T.E., Poline, J.B. et al. (2017) Scanning the horizon: towards transparent and reproducible neuroimaging research. Nat. Rev. Neurosci., 18, 115–126.
25. Pratte, M.S., Rouder, J.N., Morey, R.D. & Feng, C.N. (2010) Exploring the differences in distributional properties between Stroop and Simon effects using delta plots. Atten. Percept. Psycho., 72, 2013–2025.
26. R Core Team (2016) R: A Language and Environment for Statistical Computing. Available https://www.r-project.org/.
27. Ridderinkhof, K.R., Scheres, A., Oosterlaan, J. & Sergeant, J.A. (2005) Delta plots in the study of individual differences: new tools reveal response inhibition deficits in AD/HD that are eliminated by methylphenidate treatment. J. Abnorm. Psychol., 114, 197–215.
28. Rousselet, G.A. & Wilcox, R.R. (2016) rogme: Robust Graphical Methods for Group Comparisons. R package version 0.1.0.9000. Available https://github.com/GRousselet/rogme.
29. Rousselet, G.A., Foxe, J.J. & Bolam, J.P. (2016) A few simple steps to improve the description of group results in neuroscience. Eur. J. Neurosci., 44, 2647–2651.
30. Rousselet, G., Pernet, C. & Wilcox, R. (2017) Beyond differences in means: robust graphical methods to compare two groups in neuroscience. figshare. https://doi.org/10.6084/m9.figshare.4055970 [Epub ahead of print.]
31. Speckman, P.L., Rouder, J.N., Morey, R.D. & Pratte, M.S. (2008) Delta plots and coherent distribution ordering. Am. Stat., 62, 262–266.
32. Talebi, V. & Baker, C.L. Jr (2016) Categorically distinct types of receptive fields in early visual cortex. J. Neurophysiol., 115, 2556–2576.
33. Wagenmakers, E.J., Wetzels, R., Borsboom, D., van der Maas, H.L. & Kievit, R.A. (2012) An agenda for purely confirmatory research. Perspect. Psychol. Sci., 7, 632–638.
34. Weissgerber, T.L., Milic, N.M., Winham, S.J. & Garovic, V.D. (2015) Beyond bar and line graphs: time for a new data presentation paradigm. PLoS Biol., 13, e1002128.
35. Wickham, H. (2016) Ggplot2 : Elegant Graphics for Data Analysis Use R! Springer, Switzerland, pp. 1. online resource.
36. Wilcox, R.R. (1995) Comparing two independent groups via multiple quantiles. J. Roy. Stat. Soc. D (Sta.), 44, 91–99.
37. Wilcox, R.R. (2001) Modern insights about Pearson's correlation and least squares regression. Int. J. Select. Assess., 9, 195–205.
38. Wilcox, R.R. (2006) Graphical methods for assessing effect size: some alternatives to Cohen's d. J. Exp. Educ., 74, 353–367.
39. Wilcox, R.R. (2009). Basic Statistics : Understanding Conventional Methods and Modern Insights. Oxford University Press, New York, Oxford.
40. Wilcox, R.R. (2012) Comparing two independent groups via a quantile generalization of the Wilcoxon-Mann-Whitney test. J. Mod. Appl. Stat. Meth., 11, 296–302.
41. Wilcox, R.R. (2017) Introduction to Robust Estimation and Hypothesis Testing, 4th edn. Academic Press, San Diego, CA.
42. Wilcox, R.R. & Erceg-Hurn, D.M. (2012) Comparing two dependent groups via quantiles. J. Appl. Stat., 39, 2655–2664.
43. Wilcox, R.R. & Keselman, H.J. (2003) Modern robust data analysis methods: measures of central tendency. Psychol. Methods, 8, 254–274.
44. Wilcox, R.R. & Muska, J. (2010) Measuring effect size: a non-parametric analogue of omega(2). Brit. J. Math. Stat. Psy., 52, 93–110.
45. Wilcox, R.R., Erceg-Hurn, D.M., Clark, F. & Carlson, M. (2014) Comparing two independent groups via the lower and upper quantiles. J. Stat. Comput. Sim., 84, 1543–1551.
46. Wilke, C.O. (2016) cowplot: Streamlined Plot Theme and Plot Annotations for ‘ggplot2’. R package version 0.6.2. Available https://cran.r-project.org/package=cowplot.