distpdf.grad: Gradient of the non-normalised pdf of distances or the...
In DistanceDevelopment/mrds: Mark-Recapture Distance Sampling

distpdf.grad

R Documentation

Gradient of the non-normalised pdf of distances or the detection function for the distances.

Description

This function has been updated to match distpdf closely, so that it has the same flexibility. Effectively, it gives the gradient of distpdf or detfct, whichever one is specified.

Usage

distpdf.grad(
  distance,
  par.index,
  ddfobj,
  standardize = FALSE,
  width,
  point,
  left = 0,
  pdf.based = TRUE
)

Arguments

`distance`	vector of distances
`par.index`	the index of the parameter of interest
`ddfobj`	the ddf object
`standardize`	whether the function should return the gradient of the standardized detection function g(x)/g(0) (TRUE), or simply of g(0) (FALSE). Currently only implemented for standardize = FALSE.
`width`	the truncation width
`point`	are the data from point transects (TRUE) or line transects (FALSE).
`left`	the left truncation (default 0)
`pdf.based`	is it the gradient of the non-normalised pdf (TRUE) or the detection function (FALSE)? Default is TRUE.

Details

Various functions used to specify key and adjustment functions for gradients of detection functions.

So far, only developed for the half-normal, hazard-rate and uniform key functions in combination with cosine, simple polynomial and Hermite polynomial adjustments. It is only called by the gradient-based solver and should not be called by the general user.

distpdf.grad will call either a half-normal, hazard-rate or uniform function with adjustment terms to fit the data better, returning the gradient of detection at that distance w.r.t. the parameters. The adjustments are either cosine, Hermite or simple polynomial.