Evaluate the smoothing matrix, the radial basis matrix, the polynomial matrix and their associated coefficients
The function evaluates the smoothing matrix H, the
matrices Q and S and their associated
coefficients c and s. This function is not intended to be used directly.
dssmoother(X,Y=NULL,lambda,m,s)
X |
Matrix of explanatory variables, size n,p. |
Y |
Vector of response variable. If null, only the smoothing matrix is returned. |
lambda |
The smoothness coefficient lambda for thin plate splines of
order |
m |
The order of derivatives for the penalty (for thin plate splines it is the order). This integer m must verify 2m+2s/d>1, where d is the number of explanatory variables. |
s |
The power of weighting function. For thin plate splines s is equal to 0. This real must be strictly smaller than d/2 (where d is the number of explanatory variables) and must verify 2m+2s/d. To get pseudo-cubic splines, choose m=2 and s=(d-1)/2 (See Duchon, 1977). |
see the reference for detailed explanation of Q (the semi kernel or radial basis) and S (the polynomial null space).
Returns a list containing the smoothing matrix H, and two
matrices denoted Sgu (for null space) and Qgu.
Pierre-Andre Cornillon, Nicolas Hengartner and Eric Matzner-Lober
Duchon, J. (1977) Splines minimizing rotation-invariant semi-norms in Solobev spaces. in W. Shemp and K. Zeller (eds) Construction theory of functions of several variables, 85-100, Springer, Berlin.
C. Gu (2002) Smoothing spline anova models. New York: Springer-Verlag.
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