micompr: An R Package for Multivariate Independent Comparison of Observations

The R package micompr implements a procedure for assessing if two or more multivariate samples are drawn from the same distribution. The procedure uses principal component analysis to convert multivariate observations into a set of linearly uncorrelated statistical measures, which are then compared using a number of statistical methods. This technique is independent of the distributional properties of samples and automatically selects features that best explain their differences. The procedure is appropriate for comparing samples of time series, images, spectrometric measures or similar high-dimension multivariate observations.

[1]  Donald B. Rubin,et al.  Ensemble-Adjusted p Values , 1983 .

[2]  U. Netlogo Wilensky,et al.  Center for Connected Learning and Computer-Based Modeling , 1999 .

[3]  A. I. McLeod,et al.  Optimal Deseasonalization for Monthly and Daily Geophysical Time Series , 2012 .

[4]  Diana Adler,et al.  Using Multivariate Statistics , 2016 .

[5]  W. Kruskal,et al.  Use of Ranks in One-Criterion Variance Analysis , 1952 .

[6]  Hadley Wickham,et al.  testthat: Get Started with Testing , 2011, R J..

[7]  L. Baringhaus,et al.  On a new multivariate two-sample test , 2004 .

[8]  Agostinho C. Rosa,et al.  Model-independent comparison of simulation output , 2015, Simul. Model. Pract. Theory.

[9]  Yihui Xie,et al.  Dynamic Documents with R and knitr , 2015 .

[10]  Kenneth J. Berry,et al.  Multi-response permutation procedures for a priori classifications , 1976 .

[11]  James R. Schott,et al.  Principles of Multivariate Analysis: A User's Perspective , 2002 .

[12]  Subhabrata Chakraborti,et al.  Nonparametric Statistical Inference , 2011, International Encyclopedia of Statistical Science.

[13]  Robert V. Brill,et al.  Applied Statistics and Probability for Engineers , 2004, Technometrics.

[14]  K. Hipel,et al.  Time series modelling of water resources and environmental systems , 1994 .

[15]  Agostinho C. Rosa,et al.  Parallelization Strategies for Spatial Agent-Based Models , 2015, International Journal of Parallel Programming.

[16]  G. Zararsiz,et al.  MVN: An R Package for Assessing Multivariate Normality , 2014, R J..

[17]  Eric R. Ziegel,et al.  Engineering Statistics , 2004, Technometrics.

[18]  G. Székely,et al.  TESTING FOR EQUAL DISTRIBUTIONS IN HIGH DIMENSION , 2004 .

[19]  Tarn Duong,et al.  Closed-form density-based framework for automatic detection of cellular morphology changes , 2012, Proceedings of the National Academy of Sciences.

[20]  Jorge S. Marques,et al.  Two Systems for the Detection of Melanomas in Dermoscopy Images Using Texture and Color Features , 2014, IEEE Systems Journal.

[21]  Volker Grimm,et al.  Replicating and breaking models: good for you and good for ecology , 2015 .

[22]  Pedro M. Ferreira,et al.  PH2 - A dermoscopic image database for research and benchmarking , 2013, 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[23]  K. R. Clarke,et al.  Non‐parametric multivariate analyses of changes in community structure , 1993 .

[24]  P. Rosenbaum.  An exact distribution‐free test comparing two multivariate distributions based on adjacency , 2005 .

[25]  Marti J. Anderson,et al.  A new method for non-parametric multivariate analysis of variance in ecology , 2001 .

[26]  Agostinho C. Rosa,et al.  Towards a standard model for research in agent-based modeling and simulation , 2015, PeerJ Prepr..

[27]  B. Edmonds,et al.  Replication, Replication and Replication: Some hard lessons from model alignment , 2003, J. Artif. Soc. Soc. Simul..

[28]  Agostinho C. Rosa,et al.  Towards a standard model for research in agent-based modeling and simulation , 2015, PeerJ Prepr..

[29]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[30]  N. David.  Validating Simulations , 2014 .

[31]  F. Massey.  The Kolmogorov-Smirnov Test for Goodness of Fit , 1951 .

[32]  R. A. van den Berg,et al.  Centering, scaling, and transformations: improving the biological information content of metabolomics data , 2006, BMC Genomics.

[33]  William Rand,et al.  Making Models Match: Replicating an Agent-Based Model , 2007, J. Artif. Soc. Soc. Simul..

[34]  Ian T. Jolliffe,et al.  Principal Component Analysis , 2002, International Encyclopedia of Statistical Science.

[35]  J. Shaffer.  Multiple Hypothesis Testing , 1995 .