Dear Eva, The recent book:
Statistical Data Analysis Explained: Applied Environmental Statistics with R by C. Reimann, P. Filzmoser, R.G. Garrett, and R. Dutter; Wiley, Chichester, 2008. uses both (a modified) R Commander and robust methods, but I hope Peter Filzmoser and Rudi Dutter read also this list and can tell more. Best regards, Valentin On Tue, Aug 4, 2009 at 5:26 PM, Ian Fellows<ifell...@ucsd.edu> wrote: > Hi Eva, > > I'm not sure about Rcmdr, but I just released the Deducer package to CRAN > which uses HCCM by default with linear models. The online manual gives some > screenshots, but I have yet to write the regression page. > > Manual: > http://www.deducer.org/pmwiki/pmwiki.php?n=Main.DeducerManual > > Cheers, > Ian Fellows > > Announcement: > --------------------------------------------------------------------------- > > > Deducer 0.1 has been released to CRAN > > Deducer is designed to be a free, easy to use, alternative to proprietary > software such as SPSS, JMP, and Minitab. It has a menu system to do common > data manipulation and data analysis tasks, and an excel-like spreadsheet in > which to view and edit data frames. The goal of the project is to two fold. > > 1. Provide an intuitive interface so that non-technical users > can learn and perform analyses without programming getting > in their way. > 2. Increase the efficiency of expert R users when performing > common tasks by replacing hundreds of keystrokes with a few > mouse clicks. Also, as much as possible the GUI should not > get in their way if they just want to do some programming. > > Deducer is integrated into the Windows RGui, and the cross-platform Java > console JGR, and is also usable and accessible from the command line. > Screen shots and examples can be viewed in the online wiki manual: > > http://www.deducer.org/pmwiki/pmwiki.php?n=Main.DeducerManual > > Comments and questions are more than welcome. A discussion group has been > created for any questions or recommendations. > > http://groups.google.com/group/deducer > > Deducer Features: > > Data manipulation: > 1. Factor editor > 2. Variable recoding > 3. data sorting > 4. data frame merging > 5. transposing a data frame > 6. subseting > > Analysis: > 1. Frequencies > 2. Descriptives > 3. Contingency tables > a. Nicely formatted tables with optional > i. Percentages > ii. Expected counts > iii. Residuals > b. Statistical tests > i. chi-squared > ii. likelihood ratio > iii. fisher's exact > iv. mantel haenszel > v. kendall's tau > vi. spearman's rho > vii. kruskal-wallis > viii. mid-p values for all exact/monte carlo tests > 4. One sample tests > a. T-test > b. Shapiro-wilk > c. Histogram/box-plot summaries > 5. Two sample tests > a. T-test (student and welch) > b. Permutation test > c. Wilcoxon > d. Brunner-munzel > e. Kolmogorov-smirnov > f. Jitter/box-plot group comparison > 6. K-sample tests > a. Anova (usual and welch) > b. Kruskal-wallis > c. Jitter/boxplot comparison > 7. Correlation > a. Nicely formatted correlation matrices > b. Pearson's > c. Kendall's > d. Spearman's > e. Scatterplot paneled array > f. Circle plot > g. Full correlation matrix plot > 8.Generalized Linear Models > a. Model preview > b. Intuitive model builder > c. diagnostic plots > d. Component residual and added variable plots > e. Anova (type II and III implementing LR, Wald and F tests) > f. Parameter summary tables and parameter correlations > g. Influence and colinearity diagnostics > h. Post-hoc tests and confidence intervals > with (or without) adjustments for multiple testing. > i. Custom linear hypothesis tests > j. Effect mean summaries (with confidence intervals), and > plots > k. Exports: Residuals, Standardized residuals, Studentized > residuals, Predicted Values (linear and link), Cooks > distance, DFBETA, DFFITS, hat values, and Cov Ratio > l. Observation weights and subseting > 9. Logistic Regression > a. All GLM features > b. ROC Plot > 10. Linear Model > a. All GLM features > b. Heteroskedastic robust tests > > -----Original Message----- > From: r-sig-robust-boun...@r-project.org > [mailto:r-sig-robust-boun...@r-project.org] On Behalf Of Eva Cantoni > Sent: Tuesday, August 04, 2009 6:51 AM > To: r-sig-robust > Subject: [RsR] Rcmd and robust tools > > Hi everybody: > > within our applied undergraduate courses, we would like to teach some > robust approaches (essentially multiple regression and covariance matrix > estimation) using R and the R commander Graphical User Interface (Rcmd). > Did anybody in this list already extend the R commander to include > robust methods (either from the robust or robustbase package), or is > anybody interested in collaborating to add this facility to Rcmd ? > > Best regards, > Eva > > -- > > Dr Eva Cantoni phone : (+41) 22 379 8240 > Econométrie - Univ. Genève fax : (+41) 22 379 8299 > 40, Bd du Pont d'Arve e-mail : eva.cant...@unige.ch > CH-1211 Genève 4 http://www.unige.ch/ses/metri/cantoni > > _______________________________________________ > R-SIG-Robust@r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-sig-robust > > _______________________________________________ > R-SIG-Robust@r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-sig-robust > _______________________________________________ R-SIG-Robust@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-sig-robust
