
Last chance! 50% off unlimited learning
Sale ends in
These functions can be useful when setting up new model fitting engines that are based
on the setup function bamlss.engine.setup
. See the examples.
## Functions to extract parameter states.
get.par(x, what = NULL)
get.state(x, what = NULL)
set.par(x, replacement, what)## Function for setting starting values.
set.starting.values(x, start)
For function get.par()
and set.par()
argument x
is a
named numeric vector. For function get.state()
argument x
is an object
of the smooth.construct
list that is processed by function
bamlss.engine.setup
, i.e., which has a "state"
object.
For function set.starting.values()
argument x
is the x
list, as
returned from function bamlss.frame
.
The name of the parameter(s) that should be extracted or replaced.
The value(s) that should be used for replacement.
The named numeric vector of starting values. The name convention is based
on function parameters
.
# NOT RUN {
## Create a bamlss.frame.
d <- GAMart()
bf <- bamlss.frame(num ~ s(x1) + s(x2) + te(lon,lat), data = d, family = "gaussian")
names(bf$x$mu$smooth.construct)
## Use the setup function for
## adding state elements.
bf$x <- bamlss.engine.setup(bf$x, df = c("s(x1)" = 1, "s(x2)" = 3))
names(bf$x$mu$smooth.construct)
## Extract regression coefficients.
get.state(bf$x$mu$smooth.construct[["te(lon,lat)"]], "b")
## Extract smoothing variances.
get.state(bf$x$mu$smooth.construct[["te(lon,lat)"]], "tau2")
## More examples.
state <- bf$x$mu$smooth.construct[["te(lon,lat)"]]$state
get.par(state$parameters, "b")
get.par(state$parameters, "tau2")
state$parameters <- set.par(state$parameters, c(0.1, 0.5), "tau2")
get.par(state$parameters, "tau2")
## Setting starting values.
start <- c("mu.s.s(x1).b" = 1:9, "mu.s.s(x1).tau2" = 0.1)
bf$x <- set.starting.values(bf$x, start = start)
get.state(bf$x$mu$smooth.construct[["s(x1)"]], "b")
get.state(bf$x$mu$smooth.construct[["s(x1)"]], "tau2")
# }
Run the code above in your browser using DataLab