dmudr {assist}R Documentation

Interface of dmudr subroutine in RKPACK

Description

To calculate a spline estimate with multiple smoothing parameters

Usage

dmudr(y, q, s, weight = NULL, vmu = "v", theta = NULL, varht = NULL, 
    tol = 0, init = 0, prec = 1e-06, maxit = 30)

Arguments

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 H_0 of size (length-of-y,dim(H_0)), with elements equal to the bases of H_0 evaluated at design points.

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".

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 1e-06.

maxit

maximum number of iterations allowed. Default is 30.

Value

info

an integer that provides error message. info=-1 indicates dimension error, info=-2 indicates F_{2}^{T} Q_{*}^{θ} F_{2} !>= 0, info=-3 indicates tuning parameters are out of scope, info=-4 indicates fails to converge within maxite steps, info=-5 indicates fails to find a reasonable descent direction, 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.

theta

estimates of parameters log10(θ).

nlaht

the estimate of log10(nobs*λ).

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(H_0), number of bases.

nq

length(rk), number of reproducing kernels.

s,q,y

changed from the inputs.

Author(s)

Chunlei Ke chunlei\_ke@pstat.ucsb.edu and Yuedong Wang yuedong@pstat.ucsb.edu

References

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

dsidr, gdsidr, gdmudr, ssr


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