User manual SAS IML STUDIO 3.3

[. . . ] SAS/IML Studio 3. 3 User's Guide ® SAS® Documentation The correct bibliographic citation for this manual is as follows: SAS Institute Inc. SAS/IML® Studio 3. 3 User's Guide. SAS/IML® Studio 3. 3 User's Guide Copyright © 2010, SAS Institute Inc. , Cary, NC, USA ISBN 978-1-60764-676-1 All rights reserved. Produced in the United States of America. [. . . ] The value in this field is combined with the values in the previous two boxes to form a list of values for the LOGNLAMBDA= option. NOTE: SAS/IML Studio adds a smoother to an existing scatter plot when both of the following conditions are satisfied: The scatter plot is the active window when you select the analysis. The scatter plot variables match the analysis variables. Chapter 18, "Data Smoothing: Loess, " discusses how to display multiple smoothers in a single scatter plot and how to remove smoothers from a scatter plot. Tables Tab You can use the Tables tab to display tables that summarize the results of the analysis. The following tables are available: Data summary summarizes information about the number of observations. Fit summary summarizes the model parameters. Fit statistics summarizes the fit, including the smoothing value that optimizes the selection criterion. Output Variables Tab !303 Figure 19. 6 The Tables Tab Output Variables Tab You can use the Output Variables tab (Figure 19. 7) to add analysis variables to the data table. If you request a plot that uses one of the output variables, then that variable is automatically created even if you did not explicitly select the variable on the Output Variables tab. The following list describes each output variable and indicates how it is named. Y represents the name of the response variable. Confidence limits for means adds 95% confidence limits for the expected value (mean). The variables are named Raw residuals adds residuals, which are calculated as observed values minus predicted values. The variable is named TPSplR_Y . 304 !Chapter 19: Data Smoothing: Thin-Plate Spline Figure 19. 7 The Output Variables Tab Analysis of Selected Variables If one or more interval variables are selected in a data table when you run the analysis, then the following occurs: The first selected interval variable is automatically entered in the Y Variable field of the Variables tab. The second selected interval variable is automatically entered in the X Variable field. No role variables are used for this analysis. Chapter 20 Data Smoothing: Polynomial Regression Contents Overview of the Polynomial Regression Analysis Example: Fit a Polynomial Curve to Data . Specifying the Polynomial Regression Analysis . 305 305 309 310 310 310 311 312 314 Overview of the Polynomial Regression Analysis The Polynomial Regression analysis fits a low-order polynomial regression function to bivariate data by using ordinary least squares. This is a global parametric fit, whereas the other SAS/IML Studio smoothers are modern local nonparametric smoothers. You can run a Polynomial Regression analysis by selecting Analysis IData Smoothing IPolynomial Regression from the main menu. The computation of the regression function, confidence limits, and related statistics is implemented by calling the REG procedure in SAS/STAT software. [. . . ] The following list presents features of SAS/INSIGHT data views (tables and plots) that are not included in SAS/IML Studio. multiple plots in a single window "renewing" a plot or analysis GUI support for animation changing the orientation of plots changing the formats of table cells after the table is created saving tables to data sets after they are created changing the attributes of a curve after it is created user-defined formats a "Tools window" for rapidly changing attributes of markers and curves a mechanism to set a common view range for all plots that display a given variable multiple plots (for example, BY-group plots and scatter plot matrices) in a single window 586 !Appendix B: SAS/INSIGHT Features Not Available in SAS/IML Studio The following list presents features of SAS/INSIGHT analyses that are not included in SAS/IML Studio. adding or deleting curves, graphs, variables, and tables from existing analyses without explicitly rerunning the analysis "group" variables for the analysis of BY-groups "freezing" an analysis for easy comparison with subsequent analyses sliders for interactively varying parameters in models creating a plot of a parametric cumulative distribution function a kernel smoother for scatter plot smoothing maximum redundancy analysis biplots for many multivariate analyses Index Symbols _OBSTAT_ variable, 44 _ObsNum_ variable, 223 A accessibility, 10 action menu, 223, 287 action menus, 549 active window, 26 AddAnalysisVar method, 548 adding observations, 37 variables, 35 aggregate, 366 Air data set, 95, 572 Akaike's information criterion, 289 Analysis menu, 229 not enabled, 383 animation, 585 annotations deleting, 145 inserting, 143 properties, 146 ANOVA, 331, 479, 491 AppendActionMenuItem method, 549 aspect ratio, 147, 151, 173 Auto Close property, 561 Auto Hide property, 561 Auto Position property, 561 Auxiliary Input window, 552 axes changing range, 175 changing tick marks, 175 labels, 179 location, 122 properties, 178 setting common view range, 226 axis area, 154 axis label area, 154 B bar charts, 16, 63 properties, 66 Baseball data set, 316, 337, 416, 436, 544, 572 bin tool, 74, 142 biplots, 425, 429, 586 box plots, 22, 76 displaying means, 151 displaying notches, 151 displaying serifs, 151 properties, 78 Business data set, 84, 496, 574 BY groups, 188, 211, 213 BY variables, 211 BY-group analysis, 586 BY-group plots, 223 copying to output document, 225 layout, 225 not linked to original data, 223 writing to files, 226 C CANCORR procedure, 454 CANDISC procedure, 466 canonical components, 466 Canonical Correlation analysis, 453 Canonical Discriminant analysis, 465 canonical variables, 453 Caribbean data set, 574 CDF plot parametric, 586 CDF plots, 252, 258, 259 CentralAmerica data set, 574 changing contours, 130 chi-square residuals, 367 chi-squared ( 2 ) symbol, 186 classification criterion, 483 classification fit plots, 477, 491 classification variables, 351, 358, 374, 396 client, 559 Climate data set, 119, 127, 575 closing windows, 206 color blend, 91, 149 colors of lines, 97 of markers, 50, 91, 159 predefined, 149 column headings, 40 column variables, 503 common factors, 434 communality, 434 comparing smoothers, 284 588 ! [. . . ]