pspline.tar

代码说明：

  指定预测变量的惩罚样条基础。这是通过拟合一组相对较小的样条并惩罚积分二阶导数来完成的。传统的平滑样条曲线每次观察使用一个基础，但是一些作者指出，对于大于自由度的2-3倍的任何数量的基函数，拟合的最终结果是难以区分的。Eilers和Marx指出，如果基函数是均匀间隔的，这会导致显着的计算简化，它们将结果称为p样条。(Specifies a penalised spline basis for the predictor. This is done by fitting a comparatively small set of splines and penalising the integrated second derivative. Traditional smoothing splines use one basis per observation, but several authors have pointed out that the final results of the fit are indistinguishable for any number of basis functions greater than about 2-3 times the degrees of freedom. Eilers and Marx point out that if the basis functions are evenly spaced, this leads to significant computational simplification, they refer to the result as a p-spline.)