[RsR] Rcmd and robust tools

Hi Eva,

Thanks to Valentin for the nice remarks.

In our group we are developing a package called DAS+R on the basis of
the Rcommander. A very preliminary version is available from
http://www.statistik.tuwien.ac.at/StatDA/DASplusR/
It particularly considers spatial data and only has small attempts to
use robust methods at the moment (e.g. in generating a well scaled
background map).

We do not have much experience in using the system in teaching,
nevertheless we observed that students accept and enjoy very much the
possibility of clicking instead of learning commands by hard. So I would
be very much interested in a cooperation and exchange of ideas.

Best regards,
Rudi

Valentin Todorov wrote:
  

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 at 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 at r-project.org
[mailto:r-sig-robust-bounces at 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
 Econom?trie - Univ. Gen?ve     fax    : (+41) 22 379 8299
 40, Bd du Pont d'Arve          e-mail : Eva.Cantoni at unige.ch
 CH-1211 Gen?ve 4               http://www.unige.ch/ses/metri/cantoni