Slide presentation: Introduction to R
Fürst, G. (2011) Introduction to R : overview, basic models, and useful packages for psychologists. XIIth congress of the Swiss Psychological Society, September 12th and 13th, University of Fribourg.
Permutation Tests for Regression, (Repeated Measures) ANOVA/ANCOVA and Comparison of Signals: R package permuco available on cran: https://CRAN.R-project.org/package=permuco
Functions to compute p-values based on permutation tests. Regression, ANOVA and ANCOVA, omnibus F-tests, marginal unilateral and bilateral t-tests are available. Several methods to handle nuisance variables are implemented (Kherad-Pajouh, S., & Renaud, O. (2010) <doi:10.1016/j.csda.2010.02.015> ; Kherad-Pajouh, S., & Renaud, O. (2014) <doi:10.1007/s00362-014-0617-3> ; Winkler, A. M., Ridgway, G. R., Webster, M. A., Smith, S. M., & Nichols, T. E. (2014) <doi:10.1016/j.neuroimage.2014.01.060>). 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) <doi:10.1016/j.jneumeth.2007.03.024>) and the threshold-free cluster enhancement (Smith, S. M., & Nichols, T. E. (2009) <doi:10.1016/j.neuroimage.2008.03.061>).
Robust analyses and R software
Courvoisier, D. S. and Renaud, O. (2010), Robust analysis of the central tendency, simple and multiple regression and ANOVA: A step by step tutorial, International Journal of Psychological Research 3(1), 78--87. Available online
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
Code in R sofware for a robust coefficient of determination (or R^2)
This page used to contain a companion code in R for sofware for a robust coefficient of determination from:
Renaud, O. and Victoria-Feser, M.-P. (2010), A robust coefficient of determination for regression, Journal of Statistical Planning and Inference 140, 1852--1862. DOI: 10.1016/j.jspi.2010.01.008
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")
\--> method = "SMDM"
Residuals:
Min 1Q Median 3Q Max
-8.5458 -1.9686 0.0873 2.4699 6.4041
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -5.2269 10.6118 -0.493 0.628629
salaryP 0.5099 2.2364 0.228 0.822370
motherLev 6.2439 1.5613 3.999 0.000929 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Robust residual standard error: 4.266
Multiple R-squared: 0.509, Adjusted R-squared: 0.4512
Convergence in 11 IRWLS iterations
[...]