Guide: Building a mizer model • mizer

This guide gives an overview of the functions used to build a mizer model from a species parameter data frame. For bringing the model to steady state and calibrating it, see the guide to reaching steady state and calibrating.

Each new…/set… function returns a new MizerParams object — always reassign (params <- f(params, ...)); never modify slots in place.

Choosing a constructor

Function	Model type
`newSingleSpeciesParams()`	one species in a fixed background
`newCommunityParams()`	single size spectrum, no species identity
`newTraitParams()`	several species differing only in asymptotic size
`newMultispeciesParams()`	fully general multi-species model

Most work uses newMultispeciesParams(), driven by a species parameter data frame. The rest of this material covers that route.

Step 1 — Assemble the species parameters

species_params is a data frame with one row per species. Only two columns are truly required:

Column	Meaning
`species`	the species name
`w_inf`	von Bertalanffy asymptotic weight (g) — the maximum-size parameter

Mizer derives defaults for w_max (the computational grid boundary, default 1.5 * w_inf), w_repro_max and w_mat from w_inf. Everything else has a sensible default or is calculated.

w_inf is the parameter to supply, but it need not be the one you have: a table giving only w_max, or only lengths (l_inf or l_max, with the length–weight parameters a and b), is accepted, and mizer fills w_inf in from it and says so. Note that when w_inf is taken from w_max the two are equal, so the 1.5 * headroom above the asymptotic size is not there.

Commonly supplied:

Column	Meaning
`w_min`	Egg size (g, default 0.001)
`w_mat`	Maturity weight (g)
`beta`	Preferred predator/prey mass ratio (default 30)
`sigma`	Width of the lognormal predation kernel (default 2)
`k_vb`	von Bertalanffy K — used to derive `h` (and then `gamma`) if `h`/`gamma` absent
`h`, `gamma`	Max intake coefficient and search-volume coefficient (alternative to `k_vb`)
`a`, `b`	Length–weight conversion parameters (\(w = a l^b\), defaults 0.006 and 3)
`alpha`	Assimilation efficiency (default 0.6)
`biomass_observed`	Observed biomass, for calibration

Units: weights in grams, lengths in cm, time in years. A CSV read with read.csv() is a fine source; the package ships an example:

species_params <- read.csv(
    system.file("extdata", "NS_species_params.csv", package = "mizer"))

Step 2 — Create the MizerParams object

params <- newMultispeciesParams(species_params)

Useful optional arguments to newMultispeciesParams():

Argument	Effect
`interaction`	species × species matrix of dimensionless overlaps in `[0, 1]` (1 = full interaction, the default for every pair); scales encounter and predation mortality
`kappa`, `lambda`, `w_pp_cutoff`	resource spectrum coefficient, exponent, and cutoff size
`no_w`	number of size bins on the logarithmic grid (default 100; the size range is determined automatically from the species parameters)
`gear_params`	fishing gear definitions (usually omitted and configured later — see the guide to setting up fishing; defaults to a knife-edge gear catching every species)
`second_order_w`	use the second-order size-advection scheme; see the section “Numerical scheme: watch for numerical diffusion” in the guide to running a mizer simulation

Change gears later with gear_params(params) <- ... or setFishing() — see the guide to setting up fishing.

Step 3 — Inspect what you built

summary(params)
species_params(params)     # given + calculated, one row per species
interaction_matrix(params) # the interaction matrix
gear_params(params)        # the fishing gears
resource_params(params)    # the resource scalars

At this point newMultispeciesParams() has given you only a rough initial spectrum. The model is not yet at steady state and is not yet calibrated: that is the guide to reaching steady state and calibrating, which picks up from here.

Saving and loading a model

A finished model is worth persisting so you don’t rebuild it every session. Use mizer’s own save/restore functions rather than bare saveRDS() — they store the model in a version-stable form:

saveParams(params, "cod_model.rds")    # write a MizerParams to disk
params <- readParams("cod_model.rds")  # read it back

saveSim() and readSim() do the same for a MizerSim object. If the model needs an extension package, these are the only safe way to persist it — a bare readRDS() silently strips the extension class. See the guide to using mizer extension packages.

Before saving, record who made the model and what it is for with setMetadata(). This matters most when you share the model with others, because the metadata travels with the object:

params <- setMetadata(params,
    title = "Celtic Sea model",
    description = "A multi-species model of the Celtic Sea fish community.",
    authors = list(list(name = "Your Name", email = "you@example.com")),
    url = "https://example.com/celtic-sea-model")
getMetadata(params)     # read the metadata back

All fields are optional and you can add fields of your own. mizer also fills in mizer_version, extensions, time_created and time_modified automatically.

Quick reference

# ── Build ─────────────────────────────────────────────────────────────────────
species_params <- read.csv(
    system.file("extdata", "NS_species_params.csv", package = "mizer"))
params <- newMultispeciesParams(species_params)
params <- newTraitParams()  # or newCommunityParams(), newSingleSpeciesParams()

# ── Inspect ───────────────────────────────────────────────────────────────────
summary(params)
species_params(params)      # given + calculated, one row per species
interaction_matrix(params)  # the interaction matrix
gear_params(params)         # the fishing gears
resource_params(params)     # the resource scalars

# ── Save / reload ─────────────────────────────────────────────────────────────
params <- setMetadata(params, title = "Celtic Sea model", ...)
saveParams(params, "model.rds")
params <- readParams("model.rds")
saveSim(sim, "sim.rds")
sim <- readSim("sim.rds")