Classification effects are one way for you to define models that depend in complex ways on the values of the predictor variables. Other useful predictive relations require more complex specifications; for these you specify constructed effect parameters and use them in your model. There are four types of constructed effects:
Constructed effect parameters consists of a name, a type, and the list of constituent variables, in addition to other subparameters, as shown in Table 4.
Table 4: Models with Constructed Effects
| Spline smoothing | ||
| SAS code: | ||
effect SmoothX = spline(x); model y = SmoothX; |
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| Action specification: | ||
spline={{name='SmoothX', vars='x'}},
model={depVar='y', effects='SmoothX'}
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| Cubic polynomial | ||
| SAS code: | ||
effect Func = poly(x1 x2 x3 / degree=3); model y = Func; |
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| Action specification: | ||
polynomial={{name='Func', vars={'x1','x2','x3'}, degree=3}},
model={depVar='y', effects='Func'}
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| ANCOVA with splines | ||
| SAS code: | ||
class a; effect SmoothX = spline(x); model y = a|SmoothX; |
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| Action specification: | ||
class='a',
spline={{name='SmoothX', vars='x'}},
model={depVar='y',
effects={ {vars={'a', 'SmoothX'}, interact='BAR'} },
}
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The ANCOVA with splines example fits a different smooth spline model in the continuous predictor x for every value of the classification predictor a. It demonstrates that you can use effects that are constructed with an effects parameter in terms just as you would use a predictor variable. For more information about effects, see the section EFFECT Statement (SAS Visual Statistics: Procedures).