onewaytests: An R Package for One-Way Tests in Independent Groups Designs

One-way tests in independent groups designs are the most commonly utilized statistical methods with applications on the experiments in medical sciences, pharmaceutical research, agriculture, biology, engineering, social sciences and so on. In this paper, we present the onewaytests package to investigate treatment effects on the dependent variable. The package offers the one-way tests in independent groups designs, which include ANOVA, Welch's heteroscedastic F test, Welch's heteroscedastic F test with trimmed means andWinsorized variances, Brown-Forsythe test, Alexander- Govern test, James second order test and Kruskal-Wallis test. The package also provides pairwise comparisons, graphical approaches, and assesses variance homogeneity and normality of data in each group via tests and plots. A simulation study is also conducted to give recommendations for applied researchers on the selection of appropriate one-way tests under assumption violations. Furthermore, especially for non-R users, a user-friendly web application of the package is provided. This application is available at http://www.softmed.hacettepe.edu.tr/onewaytests.

[1]  Pablo J. Villacorta The welchADF Package for Robust Hypothesis Testing in Unbalanced Multivariate Mixed Models with Heteroscedastic and Non-normal Data , 2017, R J..

[2]  Cedric E. Ginestet ggplot2: Elegant Graphics for Data Analysis , 2011 .

[3]  Y. Benjamini,et al.  THE CONTROL OF THE FALSE DISCOVERY RATE IN MULTIPLE TESTING UNDER DEPENDENCY , 2001 .

[4]  Björn Lantz,et al.  The impact of sample non-normality on ANOVA and alternative methods. , 2013, The British journal of mathematical and statistical psychology.

[5]  Robert A. Cribbie,et al.  Effect of non-normality on test statistics for one-way independent groups designs. , 2012, The British journal of mathematical and statistical psychology.

[6]  Sanford Weisberg,et al.  An R Companion to Applied Regression , 2010 .

[7]  Comparability of the James' second-order approximation test and the Alexander and govern A statistic for non-normal heteroscedastic data , 1998 .

[8]  M Schumacher,et al.  Modelling the effects of standard prognostic factors in node-positive breast cancer , 1999, British Journal of Cancer.

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

[10]  Morton B. Brown,et al.  The Small Sample Behavior of Some Statistics Which Test the Equality of Several Means , 1974 .

[11]  James Algina,et al.  A generally robust approach for testing hypotheses and setting confidence intervals for effect sizes. , 2008, Psychological methods.

[12]  Y. Hochberg A sharper Bonferroni procedure for multiple tests of significance , 1988 .

[13]  Morton B. Brown,et al.  Robust Tests for the Equality of Variances , 1974 .

[14]  T. C. Oshima,et al.  Type I error rates for James's second-order test and Wilcox's Hm test under heteroscedasticity and non-normality , 1992 .

[15]  R. Fisher THE USE OF MULTIPLE MEASUREMENTS IN TAXONOMIC PROBLEMS , 1936 .

[16]  G. Brooks,et al.  Outlier Impact and Accommodation Methods: Multiple Comparisons of Type I Error Rates , 2016 .

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

[18]  Jj Allaire,et al.  Web Application Framework for R , 2016 .

[19]  Robert A. Cribbie,et al.  Tests for Treatment Group Equality When Data are Nonnormal and Heteroscedastic , 2007 .

[20]  Morten W Fagerland,et al.  The Wilcoxon–Mann–Whitney test under scrutiny , 2009, Statistics in medicine.

[21]  U. Ligges,et al.  Tests for Normality , 2015 .

[22]  Ralph A. Alexander,et al.  A New and Simpler Approximation for ANOVA Under Variance Heterogeneity , 1994 .

[23]  Yi-Chung Hu,et al.  A NEW FUZZY-DATA MINING METHOD FOR PATTERN CLASSIFICATION BY PRINCIPAL COMPONENT ANALYSIS , 2005, Cybern. Syst..

[24]  T. A. Bishop Heteroscedastic anova, manova, and multiple-comparisons / , 1976 .

[25]  G. S. James THE COMPARISON OF SEVERAL GROUPS OF OBSERVATIONS WHEN THE RATIOS OF THE POPULATION VARIANCES ARE UNKNOWN , 1951 .

[26]  B. L. Welch ON THE COMPARISON OF SEVERAL MEAN VALUES: AN ALTERNATIVE APPROACH , 1951 .

[27]  Y. Benjamini,et al.  Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .

[28]  Sam Weerahandi,et al.  Size performance of some tests in one-way anova , 1998 .

[29]  S. Holm A Simple Sequentially Rejective Multiple Test Procedure , 1979 .

[30]  R. Wilcox A new alternative to the ANOVA F and new results on James's second-order method , 1988 .

[31]  C. Lloyd Estimating test power adjusted for size , 2005 .

[32]  I. Parra-Frutos,et al.  Testing homogeneity of variances with unequal sample sizes , 2012, Computational Statistics.

[33]  G. S. James TESTS OF LINEAR HYPOTHESES IN UNIVERIATE AND MULTIVARIATE ANALYSIS WHEN THE RATIOS OF THE POPULATION VARIANCES ARE UNKNOWN , 1954 .

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

[35]  K. Hornik,et al.  A Lego System for Conditional Inference , 2006 .

[36]  G. Hommel A stagewise rejective multiple test procedure based on a modified Bonferroni test , 1988 .