(Negative) gradients of constraint function

The function derives the gradients of the constraint function for all model parameters, in the following order: 1. Scale parameter (if part of key function) 2. Shape parameter (if part of key function) 3. Adjustment parameter 1 4. Adjustment parameter 2 5. Etc.

Usage

flnl.constr.grad.neg(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 within 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".

Value

a matrix of gradients for all constraints (rows) w.r.t to every parameters (columns)

Details

The constraint function itself is formed of a specified number of non-linear constraints, which defaults to 20 and is specified through misc.options$mono.points. The constraint function checks whether the standardised detection function is 1) weakly/strictly monotonic at the points and 2) non-negative at all the points. flnl.constr.grad returns the gradients of those constraints w.r.t. all parameters of the detection function, i.e., 2 times mono.points gradients for every parameter.

This function mostly follows the same structure as flnl.constr in detfct.fit.mono.R.