[RsR] Rcmd and robust tools
Valentin Todorov
v@|ent|n@to @end|ng |rom gm@||@com
Tue Aug 4 22:13:31 CEST 2009
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<ifellows using 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-bounces using r-project.org
> [mailto:r-sig-robust-bounces using 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.Cantoni using unige.ch
> CH-1211 Genève 4 http://www.unige.ch/ses/metri/cantoni
>
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