Set predation kernelSource:
The predation kernel determines the distribution of prey sizes that a
predator feeds on. It is used in
getEncounter() when calculating
the rate at which food is encountered and in
calculating the rate at which a prey is predated upon. The predation kernel
can be a function of the predator/prey size ratio or it can be a function of
the predator size and the prey size separately. Both types can be set up with
setPredKernel(params, pred_kernel = NULL, reset = FALSE, ...) getPredKernel(params) pred_kernel(params) pred_kernel(params) <- value
A MizerParams object
Optional. An array (species x predator size x prey size) that holds the predation coefficient of each predator at size on each prey size. If not supplied, a default is set as described in section "Setting predation kernel".
If set to TRUE, then the predation kernel will be reset to the value calculated from the species parameters, even if it was previously overwritten with a custom value. If set to FALSE (default) then a recalculation from the species parameters will take place only if no custom value has been set.
setPredKernel(): A MizerParams object with updated predation kernel.
getPredKernel() or equivalently
pred_kernel(): An array (predator
species x predator_size x prey_size)
Kernel dependent on predator to prey size ratio
pred_kernel argument is not supplied, then this function sets a
predation kernel that depends only on the ratio of predator mass to prey
mass, not on the two masses independently. The shape of that kernel is then
determined by the
pred_kernel_type column in species_params.
The default for
pred_kernel_type is "lognormal". This will call the function
lognormal_pred_kernel() to calculate the predation kernel.
An alternative pred_kernel type is "box", implemented by the function
box_pred_kernel(), and "power_law", implemented by the function
power_law_pred_kernel(). These functions require certain species
parameters in the species_params data frame. For the lognormal kernel these
sigma, for the box kernel they are
ppmr_max. They are explained in the help pages for the kernel
functions. Except for
sigma, no defaults are set for
these parameters. If they are missing from the species_params data frame then
mizer will issue an error message.
You can use any other string for
pred_kernel_type. If for example you
choose "my" then you need to define a function
my_pred_kernel that you can
model on the existing functions like
When using a kernel that depends on the predator/prey size ratio only, mizer
does not need to store the entire three dimensional array in the MizerParams
object. Such an array can be very big when there is a large number of size
bins. Instead, mizer only needs to store two two-dimensional arrays that hold
Fourier transforms of the feeding kernel function that allow the encounter
rate and the predation rate to be calculated very efficiently. However, if
you need the full three-dimensional array you can calculate it with the
Kernel dependent on both predator and prey size
If you want to work with a feeding kernel that depends on predator mass and prey mass independently, you can specify the full feeding kernel as a three-dimensional array (predator species x predator size x prey size).
You should use this option only if a kernel dependent only on the predator/prey mass ratio is not appropriate. Using a kernel dependent on predator/prey mass ratio only allows mizer to use fast Fourier transform methods to significantly reduce the running time of simulations.
The order of the predator species in
pred_kernel should be the same
as the order in the species params dataframe in the
params object. If you
supply a named array then the function will check the order and warn if it is
## Set up a MizerParams object params <- NS_params ## If you change predation kernel parameters after setting up a model, # this will be used to recalculate the kernel species_params(params)["Cod", "beta"] <- 200 ## You can change to a different predation kernel type species_params(params)$ppmr_max <- 4000 species_params(params)$ppmr_min <- 200 species_params(params)$pred_kernel_type <- "box" plot(w_full(params), getPredKernel(params)["Cod", 100, ], type="l", log="x") ## If you need a kernel that depends also on prey size you need to define # it yourself. pred_kernel <- getPredKernel(params) pred_kernel["Herring", , ] <- sweep(pred_kernel["Herring", , ], 2, params@w_full, "*") params<- setPredKernel(params, pred_kernel = pred_kernel)