bin_average_weight: Bin-average the weight of a size-spectrum integral

Description

Prepares the weight $K(w)$ of an integral over the size spectrum so that the integral is evaluated with the quadrature scheme the model is actually using. Use this when writing your own indicator or diagnostic function; the built-in summary and indicator functions call it for you.

Usage

bin_average_weight(K, params)

Value

The weight K, bin-averaged when params@second_order_w[["bin_average"]]

is TRUE, otherwise returned unchanged.

Arguments

K: A numeric vector of weights indexed over the size grid, or a numeric array whose last dimension runs over the size grid (e.g. a species-by-size matrix or a gear-by-species-by-size array).
params: A MizerParams object whose second_order_w slot controls the gating.

Details

An integral $\int N(w) K(w)\, dw$ is discretised on mizer's finite-volume grid as $\sum_j N_j \bar K_j \Delta w_j$, where $N_j$ is the cell average of the density over bin $[w_j, w_{j+1}]$. Only the weight is approximated: $N_j$ is already a cell average and $\Delta w_j$ is exact, so neither the abundance nor the bin widths should ever be passed through this function.

Whether the point weight $K(w_j)$ is replaced by the bin average $$\bar K_j = \frac{1}{\Delta w_j}\int_{w_j}^{w_{j+1}} K(w)\,dw \approx \tfrac12\big(K(w_j) + K(w_{j+1})\big)$$ is controlled by the bin_average entry of the model's second_order_w() slot. When it is FALSE (the default) K is returned unchanged, so an indicator written with this function reproduces the left-edge Riemann sums of previous mizer versions byte-for-byte. When it is TRUE the trapezoidal bin average is returned, which is uniformly second order and exact whenever $K$ is linear in $w$ (e.g. the first moment $K = w$, for which it equals $(w_{j+1}^2 - w_j^2)/(2\Delta w_j)$).

Because the gating happens inside, always call this rather than averaging unconditionally: a hard-coded bin average silently changes the results of models that are on the default scheme.

If K is a product of several size-dependent factors, average the product and not the individual factors — the average of a product is not the product of the averages. Spawning stock biomass, for example, averages maturity * w as a single weight.

The top bin has no right-hand neighbour on the grid, so its weight is left unaveraged (one-sided); the density there is negligible, so this does not affect the second-order accuracy of the totals.

Examples

Run this code

# Biomass of each species above 10g -- what getBiomass() does internally.
params <- NS_params
K <- get_size_range_array(params, min_w = 10)   # species x size, 0/1
K <- sweep(K, 2, params@w, "*")                 # weight by w to get biomass
K <- bin_average_weight(K, params)              # gated on second_order_w
rowSums(sweep(initialN(params) * K, 2, params@dw, "*"))