pblm.control {pblm} | R Documentation |
Auxiliary for controlling the algorithm in a pblm
model
Description
This is an auxiliary function for controlling the algorithm in a pblm
model.
Usage
pblm.control(maxit = 30, maxit2 = 200, acc = 1e-07, acc2 = 1e-06,
zero.adj = 1e-06, l = NULL, restore.l = FALSE,
min.step.l = 1e-04, auto.select = FALSE, gaic.m = 2,
rss.tol = 1e-06, max.backfitting = 10, pgtol.df = 0.01,
factr.df = 1e+07, lmm.df = 5, parscale.df = 1,
max.gaic.iter = 500, pgtol.gaic = 1e-05, grad.tol = 1e-07,
factr.gaic = 1e+07, lmm.gaic = 5, parscale = 1,
conv.crit = c("dev", "pdev"))
Arguments
maxit |
maximum number of Fisher-scoring iterations. |
maxit2 |
maximum number of Newton-Raphson iterations for the inversion
|
acc |
tolerance to be used for the estimation. |
acc2 |
tolerance to be used for the inversion |
zero.adj |
adjustment factor for zeros in the probability vector |
l |
numerical, ranged in (0,1], representing the initial value of step lenght. By default |
restore.l |
logical, should the step length be restored to its initial value after each iteration? This is an experimental option and may be changed in the future. |
min.step.l |
numerical, minimum value fixed for the step length. |
auto.select |
logical, should the smoothing parameters be estimated by GAIC minimization? If |
gaic.m |
the "penalty" per parameter of the generalized AIC. By default it is 2, corresponding to the classical AIC. |
rss.tol |
tolerance for the residual sum of squares used in the backfitting algorithm. |
max.backfitting |
maximum number of backfitting iterations. |
pgtol.df |
tolerance to be used in order to get an amount of smoothing corresponding to the fixed degrees of freedom for the additive part. See argument |
factr.df |
numerical. For degrees-of-freedom optimization in the additive part. See argument |
lmm.df |
integer. For degrees-of-freedom optimization in the additive part. See argument |
parscale.df |
A vector of scaling parameters for vector lambda when optimizing lambda for fixed degrees of freedom. See argument |
max.gaic.iter |
integer. Maximum number of iterations for automatic model optimization. See argument |
pgtol.gaic |
numerical. Tolerance to be used for automatic selection of smoothing parameters. See argument |
grad.tol |
numerical. Tolerance to be used when inverting the gradient matrix. |
factr.gaic |
numerical. For automatic selection of smoothing parameters. See argument |
lmm.gaic |
integer. For automatic selection of smoothing parameters. See argument |
parscale |
A vector of scaling parameters for vector lambda for automatic model optimization. See argument |
conv.crit |
Convergence criterion for model estimation. The default is "dev", corresponding to log-likelihood maximization. Alternatively, "pdev" is concerned with maximum penalized log-likelihood. |
Value
A list with the same arguments of the function, unless unlikely specified by the user.
Author(s)
Marco Enea