![]() In the 1st graph, the regression line is quite flat, which implies that fat is not a good predictor of cholesterol. Ods find all /* restore the ODS Pick out checklist */īy default, the ODS program in SAS destinations the 2nd graph right after the very first. Product Cholesterol = MRW / CLPARM answer Give up proc glm knowledge=Have plots (only )=FitPlot Model Cholesterol = body weight / CLPARM option Ods decide on FitPlot (persist ) /* present only the in good shape plot until eventually we say in any other case */ proc glm facts=Have plots (only )=FitPlot Set sashelp.Coronary heart (obs= 500 ) hold Weight MRW Cholesterol Ods graphics / width=400px peak= 250 /* for demonstration, use small graphs */ details Have (The MRW variable is the Metropolitan Relative Body weight, which is an older edition of present-day BMI.) The adhering to SAS statements make that details and exhibit the in shape plots for every single model. Predicts cholesterol by working with the patient’s entire body-mass index. The very first design predicts a patient’s cholesterol degree by utilizing the patient’s weight. The data are 500 sufferers in a coronary heart review. ![]() It is the task of the ODS method to arrange the graphs on the printed page or the monitor no matter of the resource. Īs an illustration, let’s produce two graphs that exhibit the match of two diverse regression products. The ODS process does not care where the objects come from. The two (or a lot more) graphs can occur from any supply: two runs of the similar course of action, output from diverse strategies, or even graphs that you generate by employing PROC SGPLOT. 4 Other approaches to panel graphs in SAS.2 Use ODS Layout GRIDDED to set up graphs.
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