The LOESS Procedure
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Overview
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Getting Started
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Syntax
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Details
Missing Values Output Data Sets Data Scaling Direct versus Interpolated Fitting kd Trees and Blending Local Weighting Iterative Reweighting Specifying the Local Polynomials Smoothing Matrix Model Degrees of Freedom Statistical Inference and Lookup Degrees of Freedom Automatic Smoothing Parameter Selection Sparse and Approximate Degrees of Freedom Computation Scoring Data Sets ODS Table Names ODS Graphics
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Examples
- References
| WEIGHT Statement |
The WEIGHT statement specifies a variable in the input data set that contains values to be used as a priori weights for a loess fit.
The values of the weight variable must be nonnegative. If an observation’s weight is zero, negative, or missing, the observation is deleted from the analysis.