This is the 4th installment of the Getting Started series. The audience is the user who is new to the SG Procedures. Experienced users may also find some useful nuggets of information here. Series plots are frequently used to visualize a numeric response on the y-axis by another numeric variable on
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As we continue to process and understand the ongoing effects of the novel coronavirus, many of us have grown used to viewing COVID-19 dashboards and visualizations, including this popular coronavirus dashboard from SAS. If you are more accustomed to building graphs and visualizations using the SGPLOT and SGPANEL procedures, this
Ridgeline plots are useful for visualizing changes in the shapes of distribution over multiple groups or time periods. Let us look at an example of how we can create this plot using the SGPLOT procedure that is part of the ODS Graphics Procedures. For this example, we will plot the
Plotting just your data often helps you gain insight into how it has changed over time. But what if you want to know why it changed? Although correlation does not always imply causation, it is often useful to graph multiple things together, that might logically be related. For example, recessions
The STYLEATTRS statement in PROC SGPLOT enables you to override colors, markers, line patterns, fill patterns, and axis break patterns in ODS styles, without requiring you to change the ODS style template.
The LOESS statement in PROC SGPLOT finds a fit function while making no assumptions about the parametric form of the regression function.
This article is motivated by a recent question on the SAS Communities board. This user wants to create a series or spline plot where the attributes of the line (color, thickness) can be changed based on another variable. In this case it may be a binary variable with "0" and
You can use penalized B-splines display a smooth curve through a set of data. The PBSPLINE statement fits spline models, displays the fit function(s), and optionally displays the data values.
The REG statement fits linear regression models, displays the fit functions, and optionally displays the data values. You can fit a line or a polynomial curve. You can fit a single function or when you have a group variable, fit multiple functions.
Survival plots are automatically created by the LIFETEST procedure. These graphs are most often customized to fit the needs of SAS users. One way to create the customized survival plot is to save the generated data from the LIFETEST procedure, and then use the SGPLOT procedure to create your custom