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Dipl.Math. Dr. Haiko Lüpsen
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The following functions are provided by the author as R code for download. Available documentation:
Usage advices:
| Name | Funktion |
| box.f | Box-F-test for heterogeneous variances (2-factorial anova) |
| bf.f |
Brown & Forsythe-F-test for heterogeneous variances
(2-factorial anova) includes functions: bf.f, bf2.f, bf.main, bf.orthog, bf.fratio revised 6-2016 |
| wj.anova | Welch-James-anova for heterogeneous variances in between subject
designs (2-factorial anova) revised 9-2021 |
| wj.spanova |
Welch-James-anova for heterogeneous covariance matrices in split
plot designs (2-factorial anova) revised 6-2016 |
| box.andersen.f | F-test for nonnormal distributed dependent variables (2-factorial anova) |
| check.covar | Several tests of homogeneity of covariance matrices |
| check.sphere | Several tests for sphericity of a covariance matrix |
| ats.2 | 2-factorial analysis of variance using the procedure by Akritas, Arnold and Brunner |
| ats.3 | 3-factorial analysis of variance using the procedure by Akritas, Arnold and Brunner |
| np.anova |
factorial nonparametric analysis of variance (with and without
repeated easurements) using either - the Puri & Sen procedure (L statistic) (generalized Kruskal-Wallis- and Friedman-tests) or - the generalized van der Waerden procedure revised 6-2016 |
| art1.anova |
factorial nonparametric analysis of variance for between subjects
designs using the ART-procedure optional: a tranformation of the ranks into normal scores revised 6-2016 |
| art2.anova |
factorial nonparametric analysis of variance for pure within
subjects designs using the ART-procedure optional: a tranformation of the ranks into normal scores revised 6-2016 |
| art3.anova |
factorial nonparametric analysis of variance for mixed designs
(split plot designs) using the ART-procedure optional: a tranformation of the ranks into normal scores revised 6-2016 |
| koch.anova |
several nonparametric 2-factorial anovas for split plot designs
using the procedures by G. Koch (without assuming spherecity of the covariance matrix) revised 1-2017 |
| simple.effects |
parametric analysis of simple effects for between subject and mixed designs |
| gee.anova |
Anova-like tests for GEE and GLMM models: gee.anova - the classical Wald-test gee.robanova - robust Wald-test according to Fan & Zhang |
Haiko Lüpsen
luepsen@uni-koeln.de oder
haiko@luepsen.com
Last update 10.4.2020