gdsidr {assist}  R Documentation 
To calculate a spline estimate with single smoothing parameter for nonGaussian data.
gdsidr(y, q, s, family, vmu="v", varht=NULL, limnla=c(10, 3),
maxit=30, job=1, tol1=0, tol2=0, prec=1e06)
y 
a numerical vector representing the response, or a matrix of two columns for binomial data with the first column as the largest possible counts and the second column as the counts actually obsered. 
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 
family 
a string specifying the family of distribution. Families supported are "binary", "binomial", "poisson" and "gamma" for Bernoulli, binomial, poisson, and gamma distributions respectively. Canonical links are used except for Gamma family where a log link is used. 
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 1.0. 
limnla 
a vector of length 2, specifying a search range for the n times smoothing parameter on log10 scale. Default is (10, 3). 
maxit 
maximum number of iterations allowed for the iteration in GRKPACK. 
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 [limnla(1), limnla(2)] with the number of grids = job + 1. Default is 1. 
tol1 
the tolerance for elements of w's. Default is 0.0 which sets to square of machine precision. 
tol2 
tolerance for truncation used in ‘dsidr’. Default is 0.0 which sets to square of machine precision. 
prec 
precision requested for stopping the iteration. Default is 
info 
an integer that provides error message. info=0 indicates normal termination, info=1 indicates dimension error,
info=2 indicates 
fit 
estimate of the function at design points. 
c 
estimates of c. 
d 
estimates of d. 
resi 
vector of working residuals. 
varht 
estimate of dispersion parameter. 
nlaht 
the estimate of 
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 
lengthofy, number of observations. 
nnull 

s , qraux , jpvt 
QR decomposition of S=FR, as from Linpack ‘dqrdc’. 
q 
first 
Chunlei Ke chunlei_ke@yahoo.com and Yuedong Wang yuedong@pstat.ucsb.edu
Wahba, G. (1990). Spline Models for Observational Data. SIAM, Vol. 59.
Wang, Y. (1997). GRKPACK: Fitting Smoothing Spline ANOVA Models for Exponential Families. Communications in Statistics: Simulation and Computation, 24: 10371059.