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 use 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.
Source: R/flnl.grad.R
flnl.grad.RdThis 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 use 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.
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".