This function takes the x
object of a bamlss.frame
and adds
additional objects that are useful for model fitting engines. This is applied only
for 'regular' terms, e.g., as created by s
and te
.
For special model terms the corresponding smooth.construct
method is in charge
of this (see also the examples for function bfit
).
1 2 
x 
The 
update 
Sets the updating function for model terms, see function 
propose 
Sets the propose function for model terms, see function 
do.optim 
Adds list element 
df 
The initial degrees of freedom that should be assigned to a smooth model term,
based on the trace of the smoother matrix. Note that 
parametric2smooth 
Should parametric model terms be transformed into an artificial
smooth model term and be added to the 
... 
Currently not used. 
For each model term in the "smooth.construct"
object of the x
list (as returned
from bamlss.frame
), this function adds a named list called "state"
with the
following entries:
"parameters"
: A numeric vector. Regression coefficients are named with "b"
,
smooth variances are named with "tau2"
.
"fitted.values"
: Given the "parameters"
, the actual fitted values of
the model term.
"edf"
: Given the smoothing variances, the actual equivalent degrees of freedom
(edf) of the model term.
"do.optim"
: Should an optimizer function try to find optimum smoothing variances?
The state will be changed in each iteration and can be passed outside an updating function.
Additionally, if missing in the xt
argument of a model term (see, e.g., function
s
for xt
) the function adds the corresponding logprior and its first
and second order derivatives w.r.t. regression coefficients in functions grad()
and
hess()
.
Also, objects named "lower"
and "upper"
are added to each model term. These
indicate the lower and upper boundaries of the parameter space.
A transformed x
list, as returned from function bamlss.frame
.
bamlss.frame
, bfit
, GMCMC
,
get.par
, set.par
, get.state
1 2 3 4 5 6 7 8 9 10  d < GAMart()
bf < bamlss.frame(num ~ s(x1) + s(x2), data = d, family = "gaussian")
names(bf$x$mu$smooth.construct)
bf$x < bamlss.engine.setup(bf$x, df = c("s(x1)" = 1, "s(x2)" = 3))
names(bf$x$mu$smooth.construct)
names(bf$x$mu$smooth.construct[["s(x1)"]])
names(bf$x$mu$smooth.construct[["s(x1)"]]$state)
sapply(bf$x$mu$smooth.construct, function(x) {
c(x$state$edf, get.state(x, "tau2"))
})

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