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TPSPLINE Procedure

The TPSPLINE procedure uses the penalized least squares method to fit a nonparametric regression model. It computes thin-plate smoothing splines to approximate smooth multivariate functions observed with noise. The TPSPLINE procedure allows great flexibility in the possible form of the regression surface. In particular, PROC TPSPLINE makes no assumptions of a parametric form for the model. The following are highlights of the TPSPLINE procedure's features:

  • supports the use of multidimensional data
  • supports multiple SCORE statements
  • fits both semiparametric models and nonparametric models
  • provides options for handling large data sets
  • supports multiple dependent variables
  • enables you to choose a particular model by specifying the model degrees of freedom or smoothing parameter
  • performs BY group processing, which enables you to obtain separate analysis on grouped observations
  • creates a SAS data set that corresponds to any output table
  • automatically creates graphs by using ODS Graphics

For further details see the TPSPLINE Procedure

Examples