dsidr {assist}  R Documentation 
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 
weight 
A weight matrix for penalized weighted leastsquare: 
vmu 
a character string specifying a method for choosing the smoothing parameter. "v", "m" and "u" represent GCV, GML and UBR respectively.
"u 
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 
job 
an integer representing the optimization method used to find the smoothing parameter.
The options are job=1: goldensection search on (limnla(1), limnla(2));
job=0: goldensection search with interval specified automatically;
job >0: regular grid search on 
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

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( 
s , qraux , jpvt 
QR decomposition of S=FR, as from Linpack ‘dqrdc’. 
q 
first dim( 
Chunlei Ke chunlei_ke@yahoo.com 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, 4251.
Wahba, G. (1990). Spline Models for Observational Data. SIAM, Vol. 59.