Description Usage Arguments Details Value Author(s) References See Also
The function evaluates all the features needed for a lowrank spline smoothing. This function is not intended to be used directly.
1  lrsmoother(x,bs,listvarx,lambda,m,s,rank)

x 
Matrix of explanatory variables, size n,p. 
bs 
The type rank of lowrank splines: 
listvarx 
The vector of the names of explanatory variables 
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 pseudocubic splines, choose m=2 and s=(d1)/2 (See Duchon, 1977). 
rank 
The rank of lowrank splines. 
see the reference for detailed explanation of the matrix matrix R^1U (see reference) and smoothCon for the definition of smoothobject
Returns a list containing the smoothing matrix eigenvectors and eigenvalues
vectors
and values
, and one
matrix denoted Rm1U
and one smoothobject smoothobject
.
PierreAndre Cornillon, Nicolas Hengartner and Eric MatznerLober
Duchon, J. (1977) Splines minimizing rotationinvariant seminorms in Solobev spaces. in W. Shemp and K. Zeller (eds) Construction theory of functions of several variables, 85100, Springer, Berlin.
Wood, S.N. (2003) Thin plate regression splines. J. R. Statist. Soc. B, 65, 95114.
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