dsidr {assist}R Documentation

Interface of dsidr subroutines in RKPACK


To calculate a spline estimate with a single smoothing parameter


dsidr(y, q, s=NULL, weight=NULL, vmu="v", varht=NULL, 
limnla=c(-10, 3), job=-1, tol=0)


y a numerical vector representing the response.
q a square matrix of the same order as the length of y, with elements equal to the reproducing kernel evaluated at the design points.
s the design matrix of the null space H_0 of size (length(y),dim(H_0)), with elements equal to the bases of H_0 evaluated at design points. Default is NULL, representing an empty NULL space.
weight A weight matrix for penalized weighted least-square: (y-f)'W(y-f)+nλ J(f). Default is NULL for iid random errors.
vmu a character string specifying a method for choosing the smoothing parameter. "v", "m" and "u" represent GCV, GML and UBR respectively. "u~", only used for non-Gaussian family, specifies UBR with estimated variance. Default is "v".
varht needed only when vmu="u", which gives the fixed variance in calculation of the UBR function. Default is NULL.
limnla a vector of length 2, specifying a search range for the n times smoothing parameter on log10 scale. Default is (-10, 3).
job an integer representing the optimization method used to find the smoothing parameter. The options are job=-1: golden-section search on (limnla(1), limnla(2)); job=0: golden-section search with interval specified automatically; job >0: regular grid search on [limnla(1), limnla(2)] with #(grids) = job + 1. Default is -1.
tol tolerance for truncation used in `dsidr'. Default is 0.0, which sets to square of machine precision.


info an integer that provides error message. info=0 indicates normal termination, info=-1 indicates dimension error, info=-2 indicates F_{2}^{T} Q F_{2} !>= 0, info=-3 indicates vmu is out of scope, and info>0 indicates the matrix S is rank deficient with info=rank(S)+1.
fit fitted values.
c estimates of c.
d estimates of d.
resi vector of residuals.
varht estimate of variance.
nlaht the estimate of log10(nobs*lambda).
limnla searching range for nlaht.
score the minimum GCV/GML/UBR score at the estimated smoothing parameter. When job>0, it gives a vector of GCV/GML/UBR functions evaluated at regular grid points.
df equavilent degree of freedom.
nobs length(y), number of observations.
nnull dim(H_0), number of bases.
s,qraux,jpvt QR decomposition of S=FR, as from Linpack `dqrdc'.
q first dim(H_0) columns gives F^{T} Q F_{1}, and its bottom-right corner gives tridiagonalization of F_{2}^{T} Q F_{2}.


Chunlei Ke chunlei_ke@pstat.ucsb.edu and Yuedong Wang yuedong@pstat.ucsb.edu


Gu, C. (1989). RKPACK and its applications: Fitting smoothing spline models. Proceedings of the Statistical Computing Section, ASA, 42-51.

Wahba, G. (1990). Spline Models for Observational Data. SIAM, Vol. 59.

See Also

dmudr, gdsidr, gdmudr, ssr

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