PROC TPSPLINE with Large Data Sets

The TPSPLINE Procedure

PROC TPSPLINE with Large Data Sets

The calculation of the penalized least squares estimate is computationally intensive. The amount of memory and CPU time needed for the analysis depend on the number of unique design points, which corresponds to the number of unknown parameters to be estimated.

You can specify the D= value option in the MODEL statement to reduce the number of unknown parameters. The option groups design points by the specified range (see the D= option).

PROC TPSPLINE selects one design point from the group and treats all observations in the group as replicates of that design point. Calculation of the thin-plate smoothing spline estimates are based on the reprocessed data. The way to choose the design point from a group depends on the order of the data. Therefore, different orders of input data may result in different estimates.

This option, by combing several design points into one, reduces the number of unique design points. Therefore, it provides an approximate estimate to the original data. The value you specify determines the range used to group the data.

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