dmudr {assist} | R Documentation |
To calculate a spline estimate with multiple smoothing parameters
dmudr(y, q, s, weight = NULL, vmu = "v", theta = NULL, varht = NULL,
tol = 0, init = 0, prec = 1e-06, maxit = 30)
y |
a numerical vector representing the response. |
q |
a list, or an array, of square matrices of the same order as the length of y, which are the reproducing kernels evaluated at the design points. |
s |
the design matrix of the null space |
weight |
a weight matrix for penalized weighted least-square: |
vmu |
a character string specifying a method for choosing the smoothing parameter. "v", "m" and "u" represent GCV, GML and UBR respectively. "u |
theta |
If ‘init=1’, theta includes intial values for smoothing parameters. Default is NULL. |
varht |
needed only when vmu="u", which gives the fixed variance in calculation of the UBR function. Default is NULL. |
tol |
the tolerance for truncation in the tridiagonalization. Default is 0.0. |
init |
an integer of 0 or 1 indicating if initial values are provided for theta. If init=1, initial values are provided using theta. Default is 0. |
prec |
precision requested for the minimum score value, where precision is the weaker of the absolute and relative precisions. Default is |
maxit |
maximum number of iterations allowed. Default is 30. |
info |
an integer that provides error message. 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. |
theta |
estimates of parameters |
nlaht |
the estimate of |
score |
the minimum GCV/GML/UBR score at the estimated smoothing parameters. |
df |
equavilent degree of freedom. |
nobs |
length(y), number of observations. |
nnull |
dim( |
nq |
length(rk), number of reproducing kernels. |
s , q , y |
changed from the inputs. |
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, 42-51.
Wahba, G. (1990). Spline Models for Observational Data. SIAM, Vol. 59