calculate the weights of transitions for bmtr points

calc_dist_weights() is an internal function that takes summary stats on the relationship between points and a transition line and returns the weight that should be used for that transition.

Usage

calc_dist_weights(
  dist_to_line,
  dist_along_line,
  line_lengths,
  radius_m,
  res_m,
  kernel = c("m3", "bb", "m1", "m5", "sq"),
  gamma = 3e+10,
  kl = 9e+05,
  s1 = 200
)

Arguments

dist_to_line

How far is the point from the line (m)

dist_along_line

How far along the line is the point, after projecting it onto the line (m)

line_lengths

How long is the line (m)

radius_m

The radius of the transect at the bmtr points - used to determine the band of probability density that will be added to form the weight.

res_m

The resolution of the associated bird flow model, used to determine the nugget added to the variance to represent the uncertainty in the starting and ending location of the transition.

kernel

The kernel used for calculating the standard deviation in the probability distribution:

"m3"

(default) Matern 3/2. Driven by gamma and kl.

"m1"

Matern 1/2. Driven by gamma and kl.

"m5"

Matern 5/2. Driven by gamma and kl.

"sq"

Squared-exponential (Gaussian). Driven by gamma and kl.

"bb"

Brownian bridge. Driven by s1.

See visualize_distance_weights() to explore how these kernels and their hyperparameters shape the spread.

gamma

Spread magnitude hyperparameter (m^2, a variance) for the Matern-family and squared-exponential kernels ("m1", "m3", "m5", "sq"). Ignored for kernel = "bb". The default was tuned by eye with visualize_distance_weights().

kl

Lengthscale hyperparameter (m) for the Matern-family and squared-exponential kernels ("m1", "m3", "m5", "sq"). Ignored for kernel = "bb". The default was tuned by eye using visualize_distance_weights().

s1

Spread magnitude hyperparameter (units of sqrt(m), not m) for the "bb" kernel. Ignored for all other kernels. The default was tuned by eye against visualize_distance_weights().

Value

A vector of weights of the same length as the first three arguments.

Details

The first three arguments can all be vectors in which case the calculations will be vectorized over the corresponding elements.

This is a preliminary version of the function and will likely change.

See also

visualize_distance_weights()