R: Interface of dmudr subroutine in RKPACK

dmudr {assist}

R Documentation

Interface of dmudr subroutine in RKPACK

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 `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)+n\lambda 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`\sim`", 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.

`info`	an integer that provides error message. info=-1 indicates dimension error, info=-2 indicates `F_{2}^{T} Q_{*}^{\theta} 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(\theta)`.
`nlaht`	the estimate of `log10(nobs*\lambda)`.
`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.

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