Adaptive likelihood profiling.
Calculate 1D likelihood profiles wrt. single parameters or more generally, wrt. arbitrary linear combinations of parameters (e.g. contrasts).
tmbprofile(obj, name, lincomb, h = 1e-04, ytol = 2, ystep = 0.1, maxit = ceiling(5 * ytol/ystep), parm.range = c(-Inf, Inf), slice = FALSE, trace = TRUE, ...)
obj |
Object from |
name |
Name or index of a parameter to profile. |
lincomb |
Optional linear combination of parameters to
profile. By default a unit vector corresponding to |
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? |
trace |
Trace progress? (TRUE, or a numeric value of 1, gives basic tracing: numeric values > 1 give more information) |
... |
Unused |
Given a linear combination
t = ∑_{i=1}^n v_i θ_i
of
the parameter vector θ, 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).
data.frame with parameter and function values.
## 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)
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