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matlab toolbox for Multiscale Granger Causality: follow this link
For more information on the method or reference to cite:
Cekic, S., Grandjean, D., & Renaud, O. (2019). Multiscale Bayesian state-space model for Granger causality analysis of brain signal. Journal of Applied Statistics, 46(1), 66鈥�84.
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Permutation Tests for Regression, (Repeated Measures) ANOVA/ANCOVA and Comparison of Signals: R package permuco available on cran:
Functions to compute p-values based on permutation tests. Regression, ANOVA and ANCOVA, omnibus F-tests, marginal unilateral and bilateral t-tests are available. See the "Vignette":
Several methods to handle nuisance variables are implemented (Kherad-Pajouh, S., & Renaud, O. (2010) <> ; Kherad-Pajouh, S., & Renaud, O. (2014) <> ; Winkler, A. M., Ridgway, G. R., Webster, M. A., Smith, S. M., & Nichols, T. E. (2014) <>). An extension for the comparison of signals issued from experimental conditions (e.g. EEG/ERP signals) is provided. Several corrections for multiple testing are possible, including the cluster-mass statistic (Maris, E., & Oostenveld, R. (2007) <>) and the threshold-free cluster enhancement (Smith, S. M., & Nichols, T. E. (2009) <>).
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Companion code in R sofware for a tutorial on robustness
Courvoisier, D. S. and Renaud, O. (2010), , International Journal of Psychological Research 3(1), 78--87.
List of instructions/lines of calls for the methods presented in the article(right-click and select "Save as"). Note that the version on the editor webpage is an incorrect version that was not updated by the editor.
First dataset used: cigArt.csv
Second dataset used: creatArt.csv
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This page used to contain a companion code in R for sofware for a robust coefficient of determination (or R^2)
Renaud, O. and Victoria-Feser, M.-P. (2010), , Journal of Statistical Planning and Inference 140, 1852--1862.
However this code is now included (and improved) in the function lmrob and its summary (from the "robustbase" package). It is the implementation we highly recommand. See an example of use below:
> ## If not present, install the package
> install.packages("robustbase")
[...]
>
> ## active it
> library(robustbase)
>
> ## data to be used:
> data(coleman)
>
> ## run the robust linear regression
> col2lmrob = lmrob(Y ~salaryP+motherLev, data=coleman, setting = "KS2014")
>
>
> ## get the output, including the 2 proposed robust R2 under "Multiple R-squared" and "Adjusted R-squared"
> summary(col2lmrob)
Call:
lmrob(formula = Y ~ salaryP + motherLev, data = coleman, setting = "KS2014")
听\--> method = "SMDM"
Residuals:
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-8.5458 -1.9686听 0.0873听 2.4699听 6.4041
Coefficients:
听听听听听听听听听听听 Estimate Std. Error t value Pr(>|t|)听听 听
(Intercept)听 -5.2269听听听 10.6118听 -0.493 0.628629听听 听
salaryP听听听听听听 0.5099听听听听 2.2364听听 0.228 0.822370听听 听
motherLev听听听听 6.2439听听听听 1.5613听听 3.999 0.000929 ***
---
Signif. codes:听 0 鈥�***鈥� 0.001 鈥�**鈥� 0.01 鈥�*鈥� 0.05 鈥�.鈥� 0.1 鈥� 鈥� 1
Robust residual standard error: 4.266
Multiple R-squared:听 0.509,听听 听Adjusted R-squared:听 0.4512
Convergence in 11 IRWLS iterations
[...]