Hi All, I am seeking comments, suggestions, bugs, and users for a data analysis GUI that has just been released to CRAN. One of the first areas that I think that this GUI could be of use is in the classroom, so your comments would be very valuable to me. An online manual is available (though under construction) here:
http://www.deducer.org/pmwiki/pmwiki.php?n=Main.DeducerManual I'd appreciate any feedback or bugs. I'm particularly interested in experiences using it from within non-JGR GUI's, and under Linux. If any of you teach introductory/intermediate statistics, I'd like to know how you would feel about using it in the classroom. TIA, Ian p.s. Installation instructions: install.packages("Deducer",,"http://cran.r-project.org";) --------------------------------------------------------------------------- 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 _______________________________________________ [email protected] mailing list https://stat.ethz.ch/mailman/listinfo/r-sig-teaching
