detfct.fit.opt: Fit detection function using key-adjustment functions

View source: R/detfct.fit.opt.R

detfct.fit.optR 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)

Author(s)

Dave Miller; Jeff Laake; Lorenzo Milazzo


mrds documentation built on March 18, 2022, 5:26 p.m.