bamlss.engine.setup: BAMLSS Engine Setup Function

View source: R/optimizers.R

bamlss.engine.setupR Documentation

BAMLSS Engine Setup Function


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).


bamlss.engine.setup(x, update = "iwls", propose = "iwlsC_gp",
  do.optim = NULL, df = NULL, parametric2smooth = TRUE, ...)



The x list, as returned from function bamlss.frame, holding all model matrices and other information that is used for fitting the model.


Sets the updating function for model terms, see function bfit.


Sets the propose function for model terms, see function GMCMC.


Adds list element "do.optim" in the "state" element, see the details below.


The initial degrees of freedom that should be assigned to a smooth model term, based on the trace of the smoother matrix. Note that df can be a named numeric vector. If the names match the labels of the model terms, the corresponding df are used, e.g., df = c("s(x1)" = 1, "s(x2)" = 2) sets different dfs for each term.


Should parametric model terms be transformed into an artificial smooth model term and be added to the "smooth.construct" object within the x list? This feature is handy, since algorithms can then cycle over the "smooth.construct" object, only.


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 log-prior 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.

See Also

bamlss.frame, bfit, GMCMC, get.par, set.par, get.state


d <- GAMart()
bf <- bamlss.frame(num ~ s(x1) + s(x2), data = d, family = "gaussian")
bf$x <- bamlss.engine.setup(bf$x, df = c("s(x1)" = 1, "s(x2)" = 3))
sapply(bf$x$mu$smooth.construct, function(x) {
  c(x$state$edf, get.state(x, "tau2"))

bamlss documentation built on April 8, 2022, 9:06 a.m.