tmbprofile: Adaptive likelihood profiling.

In TMB: Template Model Builder: A General Random Effect Tool Inspired by 'ADMB'

Adaptive likelihood profiling.

Description

Calculate 1D likelihood profiles wrt. single parameters or more generally, wrt. arbitrary linear combinations of parameters (e.g. contrasts).

Usage

tmbprofile(
  obj,
  name,
  lincomb,
  h = 1e-04,
  ytol = 2,
  ystep = 0.1,
  maxit = ceiling(5 * ytol/ystep),
  parm.range = c(-Inf, Inf),
  slice = FALSE,
  adaptive = TRUE,
  trace = TRUE,
  ...
)

Arguments

obj	Object from MakeADFun that has been optimized.
name	Name or index of a parameter to profile.
lincomb	Optional linear combination of parameters to profile. By default a unit vector corresponding to name.
h	Initial adaptive stepsize on parameter axis.
ytol	Adjusts the range of the likelihood values.
ystep	Adjusts the resolution of the likelihood profile.
maxit	Max number of iterations for adaptive algorithm.
parm.range	Valid parameter range.
slice	Do slicing rather than profiling?
adaptive	Logical; Use adaptive step size?
trace	Trace progress? (TRUE, or a numeric value of 1, gives basic tracing: numeric values > 1 give more information)
...	Unused

Details

Given a linear combination

t = \sum_{i=1}^n v_i \theta_i

of the parameter vector \theta, this function calculates the likelihood profile of t. By default v is a unit vector determined from name. Alternatively the linear combination may be given directly (lincomb).

Value

data.frame with parameter and function values.

See Also

plot.tmbprofile, confint.tmbprofile

Examples

## Not run: 
runExample("simple",thisR=TRUE)
## Parameter names for this model:
## beta   beta   logsdu   logsd0

## Profile wrt. sigma0:
prof <- tmbprofile(obj,"logsd0")
plot(prof)
confint(prof)

## Profile the difference between the beta parameters (name is optional):
prof2 <- tmbprofile(obj,name="beta1 - beta2",lincomb = c(1,-1,0,0))
plot(prof2)
confint(prof2)

## End(Not run)

TMB documentation built on Nov. 27, 2023, 5:12 p.m.