detfct.fit.opt: Fit detection function using key-adjustment functions
In DistanceDevelopment/mrds: Mark-Recapture Distance Sampling

detfct.fit.opt

R Documentation

Fit detection function using key-adjustment functions

Description

Fit detection function to observed distances using the key-adjustment function approach. If adjustment functions are included it will alternate between fitting parameters of key and adjustment functions and then all parameters much like the approach in the CDS and MCDS Distance FORTRAN code. This function is called by the driver function detfct.fit, then calls optimx function.

Usage

detfct.fit.opt(ddfobj, optim.options, bounds, misc.options, fitting = "all")

Arguments

`ddfobj`	detection function object
`optim.options`	control options for optim
`bounds`	bounds for the parameters
`misc.options`	miscellaneous options
`fitting`	character string with values "all","key","adjust" to determine which parameters are allowed to vary in the fitting

Value

fitted detection function model object with the following list structure

`par`	final parameter vector
`value`	final negative log likelihood value
`counts`	number of function evaluations
`convergence`	see codes in optim
`message`	string about convergence
`hessian`	hessian evaluated at final parameter values
`aux`	a list with 20 elements maxit: maximum number of iterations allowed for optimization lower: lower bound values for parameters upper: upper bound values for parameters setlower: TRUE if they are user set bounds setupper: TRUE if they are user set bounds point: TRUE if point counts and FALSE if line transect int.range: integration range values showit: integer value that determines information printed during iteration integral.numeric if TRUE compute logistic integrals numerically breaks: breaks in distance for defined fixed bins for analysis maxiter: maximum iterations used refit: if TRUE, detection function will be fitted more than once if parameters are at a boundary or when convergence is not achieved nrefits: number of refittings mono: if TRUE, monotonicity will be enforced mono.strict: if TRUE, then strict monotonicity is enforced; otherwise weak width: radius of point count or half-width of strip standardize: if TRUE, detection function is scaled so g(0)=1 ddfobj: distance detection function object; see `create.ddfobj` bounded: TRUE if estimated parameters are at the bounds model: list of formulas for detection function model (probably can remove this)