The LOESS Procedure
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Overview
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Getting Started
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Syntax
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DetailsMissing ValuesOutput Data SetsData ScalingDirect versus Interpolated Fittingk-d Trees and BlendingLocal WeightingIterative ReweightingSpecifying the Local PolynomialsSmoothing MatrixModel Degrees of FreedomStatistical Inference and Lookup Degrees of FreedomAutomatic Smoothing Parameter SelectionSparse and Approximate Degrees of Freedom ComputationScoring Data SetsODS Table NamesODS Graphics
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Examples
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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.