vgam
are set using this function.vgam.control(all.knots = FALSE, 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.weights = FALSE, se.fit = TRUE,
trace = FALSE, wzepsilon = .Machine$double.eps^0.75,
...)all.knots=TRUE for
$n \leq 40$, and
for $n > 40$,
the number of knots is approximately
$40 + (n-40)^{0.25}$.
This increaswzepsilon. If not,
any values less than wzepsilon are replaced wit.min.criterion.VGAM, but
most family functions only implement a few of these.criterion values are within
epsilon of each other.gam function, vgam cannot handle
NAs when smoothing.s terms
in the formula.
Recycling is used if necessary.
The $i$th value is the number of B-spline coefficients to be
estimweights slot
of a "vglm" object will be saved on the object.
If not, it will be reconstructed when needed, e.g., summary.TRUE, then these can be plotted with plot(..., se = TRUE).control slot of vgam objects.vglm.control.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.weights=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 save.weights=TRUE because
the weights slot cannot be reconstructed later.
vgam,
vglm.control,
vsmooth.spline,
vglm.pneumo <- transform(pneumo, let = log(exposure.time))
vgam(cbind(normal, mild, severe) ~ s(let, df = 2), multinomial,
data = pneumo, trace = TRUE, eps = 1e-4, maxit = 10)Run the code above in your browser using DataLab