vgam.control {VGAM} | R Documentation |
Algorithmic constants and parameters for running vgam
are set using this function.
vgam.control(all.knots = FALSE, backchat = if (is.R()) FALSE else TRUE, bf.epsilon = 1e-07, bf.maxit = 30, checkwz=TRUE, criterion = names(.min.criterion.VGAM), epsilon = 1e-07, maxit = 30, na.action = na.fail, nk = NULL, save.weight = FALSE, se.fit = TRUE, trace = FALSE, wzepsilon = .Machine$double.eps^0.75, xij = NULL, ...)
In the following, we let d be the number of s
terms
in the formula.
all.knots |
logical indicating if all distinct points of
the smoothing variables are to be used as knots.
By default, all.knots=TRUE for
n <= 40, and
for n > 40,
the number of knots is approximately
40 + (n-40)^0.25.
This increases very slowly with n
so that the number of knots is approximately between 50 and 60
for large n.
|
backchat |
logical indicating if a backchat is to be used (not applicable in R). |
bf.epsilon |
tolerance used by the modified vector backfitting algorithm for testing convergence. Must be a positive number. |
bf.maxit |
maximum number of iterations allowed in the modified vector backfitting algorithm. Must be a positive integer. |
checkwz |
logical indicating whether the diagonal elements of
the working weight matrices should be checked whether they are
sufficiently positive, i.e., greater than wzepsilon . If not,
any values less than wzepsilon are replaced with this value.
|
criterion |
character variable describing what criterion is to
be used to test for convergence.
The possibilities are listed in .min.criterion.VGAM , but
most family functions only implement a few of these.
|
epsilon |
positive convergence tolerance epsilon. Roughly
speaking, the Newton-Raphson/Fisher-scoring/local-scoring iterations
are assumed to have
converged when two successive criterion values are within
epsilon of each other.
|
maxit |
maximum number of Newton-Raphson/Fisher-scoring/local-scoring iterations allowed. |
na.action |
how to handle missing values. Unlike the
SPLUS gam function, vgam cannot handle
NA s when smoothing.
|
nk |
vector of length d containing positive integers.
Recycling is used if necessary.
The ith value is the number of B-spline coefficients to be
estimated for each component function of the ith
s() term.
nk differs from the number of knots by some constant.
If specified, nk overrides the automatic knot selection
procedure.
|
save.weight |
logical indicating whether the weights slot
of a "vglm" object will be saved on the object. If not, it will
be reconstructed when needed, e.g., summary .
|
se.fit |
logical indicating whether approximate
pointwise standard errors are to be saved on the object.
If TRUE , then these can be plotted with plot(..., se=TRUE) .
|
trace |
logical indicating if output should be produced for each iteration. |
wzepsilon |
Small positive number used to test whether the diagonals of the working
weight matrices are sufficiently positive.
|
xij |
formula giving terms making up a covariate-dependent term.
|
... |
other parameters that may be picked up from control
functions that are specific to the VGAM family function.
|
Most of the control parameters are used within vgam.fit
and
you will have to look at that to understand the full details. Many of
the control parameters are used in a similar manner by vglm.fit
(vglm
) because the algorithm (IRLS) is very similar.
Setting save.weight=FALSE
is useful for some models because the
weights
slot of the object is often the largest and so less
memory is used to store the object. However, for some VGAM
family function, it is necessary to set save.weight=TRUE
because
the weights
slot cannot be reconstructed later.
A list with components matching the input names. A little error
checking is done, but not much.
The list is assigned to the control
slot of vgam
objects.
vgam
does not implement half-stepsizing, therefore parametric
models should be fitted with vglm
. Also, vgam
is
slower than vglm
too.
Thomas W. Yee
Yee, T. W. and Wild, C. J. (1996) Vector generalized additive models. Journal of the Royal Statistical Society, Series B, Methodological, 58, 481–493.
http://www.stat.auckland.ac.nz/~yee
data(pneumo) pneumo = transform(pneumo, let=log(exposure.time)) vgam(cbind(normal, mild, severe) ~ s(let, df=3), multinomial, pneumo, trace=TRUE, eps=1e-4, maxit=10)