R/deprecated.R
    set_community_model.RdThis function has been deprecated in favour of the function
newCommunityParams that sets better default values.
set_community_model( max_w = 1e+06, min_w = 0.001, z0 = 0.1, alpha = 0.2, h = 10, beta = 100, sigma = 2, q = 0.8, n = 2/3, kappa = 1000, lambda = 2 + q - n, f0 = 0.7, r_pp = 10, gamma = NA, knife_edge_size = 1000, knife_is_min = TRUE, recruitment = kappa * min_w^-lambda, rec_mult = 1, ... )
| max_w | The maximum size of the community. The   | 
    
|---|---|
| min_w | The minimum size of the community. Default value is 1e-3.  | 
    
| z0 | The background mortality of the community. Default value is 0.1.  | 
    
| alpha | The assimilation efficiency of the community. Default value 0.2  | 
    
| h | The maximum food intake rate. Default value is 10.  | 
    
| beta | The preferred predator prey mass ratio. Default value is 100.  | 
    
| sigma | The width of the prey preference. Default value is 2.0.  | 
    
| q | The search volume exponent. Default value is 0.8.  | 
    
| n | The scaling of the intake. Default value is 2/3.  | 
    
| kappa | The carrying capacity of the plankton spectrum. Default value is 1000.  | 
    
| lambda | The exponent of the plankton spectrum. Default value is 2 + q - n.  | 
    
| f0 | The average feeding level of individuals who feed on a power-law 
spectrum. This value is used to calculate the search rate parameter 
  | 
    
| r_pp | Growth rate parameter for the plankton spectrum. Default value is 10.  | 
    
| gamma | Volumetric search rate. Estimated using   | 
    
| knife_edge_size | The size at the edge of the knife-selectivity function. Default value is 1000.  | 
    
| knife_is_min | Is the knife-edge selectivity function selecting above (TRUE) or below (FALSE) the edge. Default is TRUE.  | 
    
| recruitment | The constant recruitment in the smallest size class of the
community spectrum. This should be set so that the community spectrum 
continues the plankton spectrum. Default value =   | 
    
| rec_mult | Additional multiplier for the constant recruitment. Default value is 1.  | 
    
| ... | Other arguments to pass to the   | 
    
An object of type MizerParams
This functions creates a MizerParams object so that
community-type models can be easily set up and run. A community model has
several features that distinguish it from the food-web type models. Only one
'species' is resolved, i.e. one 'species' is used to represent the whole
community. The plankton spectrum only extends to the start of the community
spectrum. Recruitment to the smallest size in the community spectrum is
constant and set by the user. As recruitment is constant, the proportion of
energy invested in reproduction (the slot psi of the returned 
MizerParams object) is set to 0. Standard metabolism has been turned 
off (the parameter ks is set to 0). Consequently, the growth rate is 
now determined solely by the assimilated food (see the package vignette for 
more details).
The function has many arguments, all of which have default values. The main 
arguments that the users should be concerned with are z0, 
recruitment, alpha and f0 as these determine the average
growth rate of the community.
Fishing selectivity is modelled as a knife-edge function with one parameter, 
knife_edge_size, which determines the size at which species are 
selected.
The resulting MizerParams object can be projected forward using 
project() like any other MizerParams object. When projecting 
the community model it may be necessary to keep a small time step size
dt of around 0.1 to avoid any instabilities with the solver. You can
check for these numerical instabilities by plotting the biomass or abundance
through time after the projection.
K. H. Andersen,J. E. Beyer and P. Lundberg, 2009, Trophic and individual efficiencies of size-structured communities, Proceedings of the Royal Society, 276, 109-114
Other deprecated functions: 
set_multispecies_model(),
set_trait_model()
if (FALSE) { params <- set_community_model(f0=0.7, z0=0.2, recruitment=3e7) sim <- project(params, effort = 0, t_max = 100, dt=0.1) plotBiomass(sim) plotSpectra(sim) }