View source: R/summary_methods.R
getYield | R Documentation |
This yield rate is given in grams per year. It is calculated at each time step saved in the MizerSim object.
getYield(object)
object |
An object of class |
The yield rate y_i(t)
for species i
at time t
is defined as
y_i(t)=\int\mu_{f.i}(w, t)N_i(w, t)w dw
where \mu_{f.i}(w, t)
is the fishing mortality of an individual of
species i
and weight w
at time t
and N_i(w, t)
is the
abundance density of such individuals. The factor of w
converts the
abundance density into a biomass density and the integral aggregates the
contribution from all sizes.
The total catch in a time period from t_1
to t_2
is the integral
of the yield rate over that period:
C = \int_{t_1}^{t2}y_i(t)dt
In practice, as the yield rate is only available
at the saved times, one can only approximate this integral by averaging over
the available yield rates during the time period and multiplying by the time
period. The less the yield changes between the saved values, the more
accurate this approximation is. So the approximation can be improved by
saving simulation results at smaller intervals, using the t_save
argument
to project()
. But this is only a concern if abundances change quickly
during the time period of interest.
If called with a MizerParams object, a vector with the yield rate in grams per year for each species in the model. If called with a MizerSim object, an array (time x species) containing the yield rate at each time step for all species.
getYieldGear()
Other summary functions:
getBiomass()
,
getDiet()
,
getGrowthCurves()
,
getN()
,
getSSB()
,
getYieldGear()
yield <- getYield(NS_sim)
yield[c("1972", "2010"), c("Herring", "Cod")]
# Running simulation for another year, saving intermediate time steps
params <- setInitialValues(getParams(NS_sim), NS_sim)
sim <- project(params, t_save = 0.1, t_max = 1,
t_start = 2010, progress_bar = FALSE)
# The yield rate for Herring decreases during the year
getYield(sim)[, "Herring"]
# We get the total catch in the year by averaging over the year
sum(getYield(sim)[1:10, "Herring"] / 10)
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