R: Adaptive likelihood profiling.

tmbprofile {TMB} | R Documentation

## 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, 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?
`trace` | Trace progress?
`...` | Unused

### Details

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`).

### 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)

[Package TMB version 1.7.11 Index]