![]() ![]() THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. Always use the most current release of the software. This software is offered to the research community free of charge and "as is." The user accepts all responsibility for any negative consequences that might result from its use. When bugs are found or reported, they are eliminated as quickly as possible. The copyright holder requests that this software be distributed by directing users to where the latest release of the software and documentation is archived and can be downloaded.Īs with all statistical software, all attempts are made to make sure that the computations programmed are performed correctly. This software should not be posted or stored on any webpage, server, or directory accessible to the public whether free or for a charge unless written permission has been granted by the copyright holder. Distribution after modification is prohibited, as is its use or distribution for any commercial purpose without authorization from the copyright holder. Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to use the software in this form. GRAPHDATASET NAME="graphdataset" VARIABLES=xa y1 PRE_1 LICI_1 UICI_1ĭATA: PRE_1=col(source(s), name("PRE_1"))ĭATA: LICI_1=col(source(s), name("LICI_1"))ĭATA: UICI_1=col(source(s), name("UICI_1"))ĮLEMENT: area.difference(position((xa*(LICI_1 UICI_1))), color.interior(color.lightgrey), transparency.interior(transparency."0.5"))ĮLEMENT: line(position(xa*PRE_1), color(color.PROCESS and its associated files copyright © 2012-2021 by Andrew F. *Now I can plot the observed, predicted, and the intervals. METHOD=ENTER xa /METHOD=ENTER splinex1 splinex2įormats y1 xa PRE_1 LICI_1 UICI_1 (F2.0). *Example of there use - data example taken from. Below is an example of utilizing the default knot locations, and a subsequent plot of the 95% prediction intervals and predicted values superimposed on a scatterplot.įILE HANDLE macroLoc /name = "D:\Temp\Restricted_Cubic_Splines". RCS need at least three knots, because they are restricted to be linear in the tails, and so will return k – 2 bases (where k is the number of knots). Or you can specify the exact locations of the knots. It takes either an arbitrary number of knots, and places them at the default locations according to quantiles of x’s. So here is the SPSS MACRO, and below is an example of its implementation. I’ve largely based my implementation around the various advice Frank Harell has floating around the internet (see the rcspline function in his HMisc R package), although I haven’t read his book (yet!!). See Durrleman and Simon (1989) for a simple intro. Splines are useful exploratory tools to model non-linear relationships by transforming the independent variables in multiple regression equations. Splines are useful tools to model non-linear relationships. I’ve made a macro to estimate restricted cubic spline (RCS) basis in SPSS. ![]()
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