12/17/2023 0 Comments Sas robustreg fitplot![]() The data and a scatter plot smoother are shown in the adjacent graph. This example fits the MPG_CITY variable as a function of the WEIGHT variable for vehicles in the Sashelp.Cars data set. If you a fitting a generalized linear model or a mixed model, you can use PROC GLIMMIX. For example, if you have a binary response you can use the EFFECT statement in PROC LOGISTIC. This section provides an example of using splines in PROC GLMSELECT to fit a GLM regression model.īecause the functionality is contained in the EFFECT statement, the syntax is the same for other procedures. Regression with restricted cubic splines in SAS If you are not familiar with splines and knots, read the overview article "Understanding splines in the EFFECT statement." You can also read the documentation for the EFFECT statement, which includes an overview of spline effects as well as a brief description of restricted cubic splines, which are also called "natural cubic splines." I think the fact that the SAS documentation refers to the restricted cubic splines as "natural cubic splines" has prevented some practitioners from realizing that SAS supports restricted cubic splines. Because some older procedures (such as PROC REG) do not support the EFFECT statement, the article also shows how to use SAS procedures to generate the spline basis, just like the %RCSPLINE macro does. This article provides examples of using splines in regression models. Since SAS 9.3, many SAS regression procedures provide a native implementation of restricted cubic splines by using the EFFECT statement in SAS. ![]() They then use those basis variables in a SAS regression procedure. ![]() However, the presenters have all used the %RCSPLINE macro (Frank Harrell, 1988) to generate a SAS data set that contains new variables for the spline basis functions. I have attended multiple SAS Global Forum presentations that show how to use restricted cubic splines in SAS regression procedures. Restricted cubic splines are a powerful technique for modeling nonlinear relationships by using linear regression models.
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