Date: Mon, 17 Oct 2011 12:17:39 -0700
Reply-To: "Parise, Carol A." <PariseC@sutterhealth.org>
Sender: "SPSSX(r) Discussion" <SPSSX-L@LISTSERV.UGA.EDU>
From: "Parise, Carol A." <PariseC@sutterhealth.org>
Subject: Re: splines in mixed models
In-Reply-To: <OFFB566295.8692EB3D-ON8725792C.0067FF64-8725792C.00685516@us.ibm.com>
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Jon,
I considered transforming the age variable but tried to figure out how i could make a statment about what is happening within a specified age range?
I've got 5 age/spline variables with knots that I have predetermined. My original question is with regards to the usefullness of the original variable if i have already determined that in its original form, it does not fit the model.
Thank you!
Carol
________________________________
From: Jon K Peck [mailto:peck@us.ibm.com]
Sent: Monday, October 17, 2011 12:00 PM
To: Parise, Carol A.
Cc: SPSSX-L@listserv.uga.edu
Subject: Re: [SPSSX-L] splines in mixed models
In this parameterization, you need all three variables. xa gives the linear effect, xb gives the first delta starting at 15, and xb gives the second delta starting at 25. So above 25, the effect is the sum of all three coefficients, for example.
This, of course, is just a linear spine. Often people prefer to use cubic splines, which give a smoother effect.
You could, of course, just use a functional transformation of the independent variable, for example, a cubic polynomial to get varying slopes.
Jon Peck (no "h")
Senior Software Engineer, IBM
peck@us.ibm.com
new phone: 720-342-5621
From: "Parise, Carol A." <PariseC@sutterhealth.org>
To: SPSSX-L@listserv.uga.edu
Date: 10/17/2011 11:52 AM
Subject: [SPSSX-L] splines in mixed models
Sent by: "SPSSX(r) Discussion" <SPSSX-L@listserv.uga.edu>
________________________________
hi all,
I have a model where I want to know the effect of age on the DV *within* 5 quintiles of age on time. The nature of the relationship of age with time in this study is that younger people don't slow down as much as older people, so entering age as a continuous varible doesn't make sense since this assumes a consistent association with time across all ages. The goal is to be able to say something like:
"In the youngest quintile, a one year increase in age was associated with a 0.25 hour increase in time whereas in the highest quintile, a one unit increase in age was asociated with a 1 hour increase in time."
This lead to a search to figure out how to compute splines with knots at the highest age of each of the age quintiles in my model.
I found this reference on Raynauld's site http://www.spsstools.net/Syntax/RegressionRepeatedMeasure/PiecewiseRegression.txt. The example was a linear model with 2 knots, one at xA=15 and one at xA=25. You compute:
COMPUTE xb1=xa-15
COMPUTE xc1=xa-25.
recode xb1 xc1 (lo thru 0 =0).
once i ran frequencies on this, it made sense as to how i could enter these variables into the model and interpret them as i stated above.
It then states to enter them into linear regression by entering:
/Method = enter xa
/method= enter xb1
/method = enter xc1
My 2 questions:
1) Do you need to enter the original xa variable in the model with the spline variables and the other fixed variables? I can't quite understand why this is necessary.
2) I'm assuming the answer is YES, but i just want to check - can i enter these spline variables into a mixed model and interpret them the same way?
thank you!
Carol
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