gcv.ubre_grad: The GCV/UBRE score value and its gradient

In scam: Shape Constrained Additive Models

The GCV/UBRE score value and its gradient

Description

For the estimation of the SCAM smoothing parameters the GCV/UBRE score is optimized outer to the Newton-Raphson procedure of the model fitting. This function returns the value of the GCV/UBRE score and calculates its first derivative with respect to the log smoothing parameter using the method of Wood (2009).

The function is not normally called directly, but rather service routines for bfgs_gcv.ubre.

Usage

gcv.ubre_grad(rho, G, env, control)

Arguments

rho	log of the initial values of the smoothing parameters.
G	a list of items needed to fit a SCAM.
env	Get the enviroment for the model coefficients, their derivatives and the smoothing parameter.
control	A list of fit control parameters as returned by scam.control.

Value

A list is returned with the following items:

dgcv.ubre	The value of GCV/UBRE gradient.
gcv.ubre	The GCV/UBRE score value.
scale.est	The value of the scale estimate.
object	The elements of the fitting procedure monogam.fit for a given value of the smoothing parameter.
dgcv.ubre.check	If check.analytical=TRUE this returns the finite-difference approximation of the gradient.
check.grad	If check.analytical=TRUE this returns the relative difference (in and finite differenced derivatives.

Author(s)

Natalya Pya <[e-mail]>

References

Pya, N. and Wood, S.N. (2015) Shape constrained additive models. Statistics and Computing, 25(3), 543-559

Pya, N. (2010) Additive models with shape constraints. PhD thesis. University of Bath. Department of Mathematical Sciences

Wood S.N. (2006) Generalized Additive Models: An Introduction with R. Chapman and Hall/CRC Press.

Wood, S.N. (2011) Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models. Journal of the Royal Statistical Society: Series B. 73(1): 1–34

See Also

scam, scam.fit, bfgs_gcv.ubre

scam documentation built on June 22, 2024, 10:43 a.m.