Permutation Tests for Regression, (Repeated Measures) ANOVA/ANCOVA and Comparison of Signals.

permuco

This package provides functions to compute permutation tests in linear models with nuisances variables. The package has several goals :

• Providing to users the most recent methods to handle nuisance variables for permutation tests in the linear models.
• Giving to users tools to compute most common tests in linear model (t-test, ANOVA and repeated measures ANOVA).
• Providing an extension for the multiple comparisons problem in linear models with a focus for EEG data.

See Reference for more information on the function or check the article presenting the package.

The lmperm() function

This function is constructed as an extension of the the lm() function for permutation test. It produces t statistics with univariate and bivariate p-value by permutation.

The aovperm() function

This function is constructed as an extension of the the aov() function for permutation test. It produces marginal F statistics (type III) for factorial ANOVA and ANCOVA. Moreover, repeated measures ANOVA can be perform using the same notations used in an aov() formula with +Error(id/within) to specify the random effects.

The clusterlm() function

This function compute cluster-mass statistics for multiple comparisons. It is designed for ERP analysis of uni-channel EEG data. The left part of formula object must be a matrix or dataframe which columns represents multiple responses tested on the same experimental design (specified by right part of the formula). This function provides several methods to handle nuisance variables, a F or t statistics, an extension for repeated measures ANOVA and several methods for the multiple comparisons like the threshold-free cluster enhancement.

Contact

If you need help to use the package or want to report errors, contact Jaromil Frossard at [email protected].

References

For permutation tests with nuisance variables :

• Kherad-Pajouh, S., & Renaud, O. (2010). An exact permutation method for testing any effect in balanced and unbalanced fixed effect ANOVA. Computational Statistics & Data Analysis, 54(7), 1881-1893.

• Winkler, A. M., Ridgway, G. R., Webster, M. A., Smith, S. M., & Nichols, T. E. (2014). Permutation inference for the general linear model. Neuroimage, 92, 381-397.

For permutation test in repeated measures ANOVA :

• Kherad-Pajouh, S., & Renaud, O. (2015). A general permutation approach for analyzing repeated measures ANOVA and mixed-model designs. Statistical Papers, 56(4), 947-967.

For cluster-mass statistics for the muliple comparison problems :

• Maris, E., & Oostenveld, R. (2007). Nonparametric statistical testing of EEG-and MEG-data. Journal of neuroscience methods, 164(1), 177-190.

For the threshold-free cluster-enhancement method :

• Smith, S. M., & Nichols, T. E. (2009). Threshold-free cluster enhancement: addressing problems of smoothing, threshold dependence and localisation in cluster inference. Neuroimage, 44(1), 83-98.

Academic works using permuco

• Allen, S. L., Bonduriansky, R., & Chenoweth, S. F. (2018). Genetic constraints on microevolutionary divergence of sex-biased gene expression. Phil. Trans. R. Soc. B, 373(1757), 20170427.

• Almodóvar-Rivera, I., & Maitra, R. (2019). FAST adaptive smoothing and thresholding for improved activation detection in low-signal fMRI. IEEE transactions on medical imaging.

• Bürki, A., Frossard, J., & Renaud, O. (2018). Accounting for stimulus and participant effects in event-related potential analyses to increase the replicability of studies. Journal of neuroscience methods, 309, 218-227.

• Christie, B. R., Trivino‐Paredes, J., Pinar, C., Neale, K. J., Meconi, A., Reid, H., & Hutton, C. P. (2019). A Rapid Neurological Assessment Protocol for Repeated Mild Traumatic Brain Injury in Awake Rats. Current protocols in neuroscience, 89(1), e80.

• Destro, G. F. G., de Fernandes, V., de Andrade, A. F. A., De Marco, P., & Terribile, L. C. (2019). Back home? Uncertainties for returning seized animals to the source‐areas under climate change. Global change biology.

• Drexl, K., Kunze, A. E., & Werner, G. G. (2019). The German version of the Fear of Sleep Inventory-Short Form: A psychometric study. European Journal of Trauma & Dissociation.

• Godfrey, M., Hepburn, S., Fidler, D. J., Tapera, T., Zhang, F., Rosenberg, C. R., & Lee, N. R. (2019). Autism spectrum disorder (ASD) symptom profiles of children with comorbid Down syndrome (DS) and ASD: A comparison with children with DS-only and ASD-only. Research in Developmental Disabilities, 89, 83-93.

• Hartmann, M., Sommer, N. R., Diana, L., Müri, R. M., & Eberhard-Moscicka, A. K. (2018). Further to the right: Viewing distance modulates attentional asymmetries (‘pseudoneglect’) during visual exploration. Brain and Cognition.

• Kern, E. M. A., & Langerhans, R. B. (2019). Urbanization Alters Swimming Performance of a Stream Fish. Front. Ecol. Evol. 6: 229. doi: 10.3389/fevo.

• Musariri, T., Pegg, N., Muvengwi, J., & Muzama, F. (2018). Differing patterns of plant spinescence affect blue duiker (Bovidae: Philantomba monticola) browsing behavior and intake rates. Ecology and Evolution.

• Podofillini, S., Cecere, J. G., Griggio, M., Corti, M., Capua, E. L. D., Parolini, M., … & Rubolini, D. (2019). Benefits of extra food to reproduction depend on maternal condition. Oikos.

• Soler, J., Arias, B., Moya, J., Ibáñez, M. I., Ortet, G., Fañanás, L., & Fatjó-Vilas, M. (2019). The interaction between the ZNF804A gene and cannabis use on the risk of psychosis in a non-clinical sample. Progress in Neuro-Psychopharmacology and Biological Psychiatry, 89, 174-180.

• Swanson, K., Goldbach, H. C., & Laubach, M. (2019). The rat medial frontal cortex controls pace, but not breakpoint, in a progressive ratio licking task. Behavioral neuroscience, 133(4), 385.