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README.Rmd
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README.Rmd
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---
output: github_document
---
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[](https://CRAN.R-project.org/package=permuco)
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# `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](https://jaromilfrossard.github.io/permuco/reference/) for more information on the function or check the [article](https://raw.githubusercontent.com/jaromilfrossard/permuco/master/vignettes/permuco_tutorial.pdf) 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 <jaromil.frossard@unige.ch>.
# 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.