XploRe: An Interactive Statistical Computing Environment

I A Beginner’s Course.- 1 Un Amuse-Gueule.- 2 An XploRe Tutorial.- 2.1 Getting Started.- 2.2 Two-Dimensional Plots.- 2.3 Creating a Macro.- 2.4 The Interactive Help System.- 2.5 Three-Dimensional Plots.- 2.6 Reading and Writing Data.- 3 The Integrated Working Environment.- 3.1 Introduction.- 3.2 The Editor.- 3.3 How to Run and Debug a Program.- 3.4 The Context-Sensitive Help System.- 3.5 Graphic Tools in XploRe.- 3.6 Multiple Window Displays.- 3.7 Manipulating Windows.- 3.8 How to Print a Graphic.- 3.9 The Static 2-D Window.- 3.10 The Dynamic 3-D Window.- 3.11 The Boxplot Window.- 3.12 The Flury Faces Window.- 3.13 How to Use and Create Libraries.- II XploRe in Use.- 4 Graphical Aids for Statistical Data Analysis.- 4.1 Introduction.- 4.2 First Pictures.- 4.3 Stratifications.- 5 Density and Regression Smoothing.- 5.1 Introduction.- 5.2 Density Estimation.- 5.3 Nadaraya-Watson Nonparametric Regression.- 5.4 Local Polynomial Fitting.- 5.5 Estimation of Regression Derivatives.- 5.6 Variable Amount of Smoothing.- 6 Bandwidth Selection in Density Estimation.- 6.1 Introduction.- 6.2 Choosing the Smoothing Parameter.- 6.3 Density Estimation in Action.- 7 Interactive Graphics for Teaching Simple Statistics.- 7.1 Introduction.- 7.2 The General Structure of the Teachware System.- 7.3 Description of the Main Macros in the Module.- 7.4 Details on XploRe Language.- 7.5 Conclusions.- 8 XClust: Clustering in an Interactive Way.- 8.1 Introduction.- 8.2 Cluster Analysis and Classification.- 8.3 The K-Means Method in XploRe.- 8.4 The Adaptive K-Means Method.- 8.5 The Hierarchical Cluster Analysis.- 8.6 Classification and Regression Tree (CART).- 8.7 The Investigation of the Stability of Adaptive Weights.- 9 Exploratory Projection Pursuit.- 9.1 Introduction.- 9.2 Projection Pursuit Indices.- 9.3 The Index Functions in Practice.- 9.4 What Will Be Found on Real Data?.- 9.5 Computational Aspects.- 9.6 The PPEXPL Macro.- 10 Generalized Linear Models.- 10.1 Introduction.- 10.2 Some Theory.- 10.3 Implementation.- 10.4 Example for a Gamma Model.- 10.5 Negative Binomial Regression.- 10.6 GLM Extensions: Parametric Survival Models.- 11 Additive Modeling.- 11.1 Introduction.- 11.2 Generalized Additive Models.- 11.3 Sliced Inverse Regression.- 11.4 Average Derivative Estimation.- 12 Comparing Parametric and Semiparametric Binary Response Models.- 12.1 Introduction.- 12.2 The Data.- 12.3 Parametric and Semiparametric Binary Response Models.- 12.4 Estimation.- 12.5 Testing the Adequacy of the Logit Link.- 12.6 Summary and Conclusions.- 13 Approximative Methods for Regression Models with Errors in Covariates.- 13.1 Introduction.- 13.2 Regression Calibration.- 13.3 Simulation and Extrapolation (SIMEX).- 14 Nonlinear Time Series Analysis.- 14.1 Introduction.- 14.2 Parametric Approaches.- 14.3 Nonparainetric Approach.- 14.4 Nonlinearity Tests.- 14.5 Nonlinear Prediction.- 15 Un Digestif.- 15.1 A Whole Bunch of Pictures.- 15.2 Interactive Contouring.- A Questions to XploRe.- B Language Reference.- B.1 Operators.- B.2 Flow Control.- B.3 Commands.- References.- Author Index.