flnl.grad: Gradient of the negative log likelihood function
In DistanceDevelopment/mrds: Mark-Recapture Distance Sampling

flnl.grad

R Documentation

Gradient of the negative log likelihood function

Description

This function derives the gradients of the negative log likelihood function, with respect to all parameters. It is based on the theory presented in Introduction to Distance Sampling (2001) and Distance Sampling: Methods and Applications (2015). It is not meant to be called by users of the mrds and Distance packages directly but rather by the gradient-based solver. This solver is used when our distance sampling model is for single-observer data coming from either line or point transect and only when the detection function contains an adjustment series but no covariates. It is implement for the following key + adjustment series combinations for the detections function: the key function can be half-normal, hazard-rate or uniform, and the adjustment series can be cosine, simple polynomial or Hermite polynomial. Data can be either binned or exact, but a combination of the two has not been implemented yet.

Usage

flnl.grad(pars, ddfobj, misc.options, fitting = "all")

Arguments

`pars`	vector of parameter values for the detection function at which the gradients of the negative log-likelihood should be evaluated
`ddfobj`	distance sampling object
`misc.options`	a list object containing all additional information such as the type of optimiser or the truncation width, and is created by `ddf.ds`
`fitting`	character string with values "all", "key", "adjust" to determine which parameters are allowed to vary in the fitting. Not actually used. Defaults to "all".