Mizer provides summary() methods for model objects and for the specialised
array classes returned by many mizer functions.
Usage
# S3 method for class 'ArraySpeciesBySize'
summary(object, all.sizes = FALSE, ...)
# S3 method for class 'ArrayTimeBySpecies'
summary(object, ...)
# S3 method for class 'ArrayTimeBySpeciesBySize'
summary(object, all.sizes = FALSE, ...)
# S3 method for class 'MizerSim'
summary(object, ...)
# S3 method for class 'MizerParams'
summary(object, ...)Arguments
- object
The object to summarise.
- all.sizes
If
FALSE(the default), values outside a species' size range (w_mintow_max) are left out, as inplot(). Only for the classes with a size dimension.- ...
Further arguments. They are currently ignored by the mizer methods.
Value
For MizerParams() and MizerSim(), the object is returned invisibly.
For array objects, a list of class summary.ArraySpeciesBySize,
summary.ArrayTimeBySpecies or summary.ArrayTimeBySpeciesBySize.
Details
For a MizerParams() object, summary() prints the model metadata, size
grids, selected species parameters and fishing gear details. For a
MizerSim() object, it first prints the parameter summary and then reports
the simulated time period and output interval.
For ArraySpeciesBySize(), ArrayTimeBySpecies() and
ArrayTimeBySpeciesBySize() objects, summary() returns a small list with
the value name, units, dimensions and a per-species data frame containing
minimum, mean and maximum values. Printing that summary object gives the same
compact table in a human-readable form.
For the two classes that have a size dimension, those values are taken over
each species' own size range, from its w_min to its w_max, which is the
range plot() draws. A rate array is defined on the whole size grid, but the
values outside a species' range describe an animal that does not exist — the
encounter rate a 40 kg Sprat would have — and they are usually the extreme
ones, so a summary that included them reported the size grid rather than the
species. Pass all.sizes = TRUE for the whole grid.
Examples
# \donttest{
summary(NS_params)
#> ℹ No `a` column so using a = 0.01 in w = a l^b, with w in g and l in cm.
#> ℹ No `b` column so using the isometric default b = 3 in w = a l^b.
#> An object of class "MizerParams"
#> mizer version: 3.0.0.9003
#> Created: 2021-09-03 21:29:38
#> Modified: 2026-06-24 14:50:03
#> Consumer size spectrum:
#> minimum size: 0.001
#> maximum size: 39851.3
#> no. size bins: 100
#> Resource size spectrum:
#> minimum size: 2.12182e-13
#> maximum size: 9.82091
#> no. size bins: 179 (226 size bins in total)
#> Steady state:
#> biomass drift: 0.014 /year (at steady state)
#> Species details:
#> An object of class "species_params" containing parameters for 12 species:
#> species w_inf w_mat w_min f0 beta sigma
#> Sprat 33.0 13 0.001 0.6 51076 0.8
#> Sandeel 36.0 4 0.001 0.6 398849 1.9
#> N.pout 100.0 23 0.001 0.6 22 1.5
#> Herring 334.0 99 0.001 0.6 280540 3.2
#> Dab 324.0 21 0.001 0.6 191 1.9
#> Whiting 1192.0 75 0.001 0.6 22 1.5
#> Sole 866.0 78 0.001 0.6 381 1.9
#> Gurnard 668.0 39 0.001 0.6 283 1.8
#> Plaice 2976.0 105 0.001 0.6 113 1.6
#> Haddock 4316.5 165 0.001 0.6 558 2.1
#> Cod 39851.3 1606 0.001 0.6 66 1.3
#> Saithe 39658.6 1076 0.001 0.6 40 1.1
#>
#> Fishing gear details:
#> Gear Effort Target species
#> ----------------------------------
#> Industrial 0.00 Sprat, Sandeel, N.pout
#> Pelagic 1.00 Herring
#> Beam 0.50 Dab, Sole, Plaice
#> Otter 0.50 Whiting, Gurnard, Haddock, Cod, Saithe
summary(NS_sim)
#> An object of class "MizerSim"
#> Parameters:
#> An object of class "MizerParams"
#> mizer version: 3.0.0.9003
#> Created: 2021-09-03 21:30:02
#> Modified: 2026-06-24 14:50:03
#> Consumer size spectrum:
#> minimum size: 0.001
#> maximum size: 39851.3
#> no. size bins: 100
#> Resource size spectrum:
#> minimum size: 8.72744e-13
#> maximum size: 9.82091
#> no. size bins: 171 (218 size bins in total)
#> Steady state:
#> biomass drift: 0.91 /year (not at steady state, largest in Cod - run tuneSteadyState())
#> Species details:
#> An object of class "species_params" containing parameters for 12 species:
#> species w_inf w_mat w_min beta sigma
#> Sprat 33.0 13 0.001 51076 0.8
#> Sandeel 36.0 4 0.001 398849 1.9
#> N.pout 100.0 23 0.001 22 1.5
#> Herring 334.0 99 0.001 280540 3.2
#> Dab 324.0 21 0.001 191 1.9
#> Whiting 1192.0 75 0.001 22 1.5
#> Sole 866.0 78 0.001 381 1.9
#> Gurnard 668.0 39 0.001 283 1.8
#> Plaice 2976.0 105 0.001 113 1.6
#> Haddock 4316.5 165 0.001 558 2.1
#> Cod 39851.3 1606 0.001 66 1.3
#> Saithe 39658.6 1076 0.001 40 1.1
#>
#> Fishing gear details:
#> Gear Effort Target species
#> ----------------------------------
#> Sprat 0.51 Sprat
#> Sandeel 0.56 Sandeel
#> N.pout 0.51 N.pout
#> Herring 1.29 Herring
#> Dab 0.93 Dab
#> Whiting 0.73 Whiting
#> Sole 1.14 Sole
#> Gurnard 0.28 Gurnard
#> Plaice 0.93 Plaice
#> Haddock 0.68 Haddock
#> Cod 0.94 Cod
#> Saithe 0.74 Saithe
#> Note: effort varied over time for Sprat (0.00 to 1.76), Sandeel (0.00 to 1.48), N.pout (0.00 to 1.76), Herring (0.12 to 3.33), Dab (0.40 to 1.40), Whiting (0.30 to 1.07), Sole (0.76 to 1.58), Gurnard (0.00 to 1.00), Plaice (0.40 to 1.40), Haddock (0.18 to 1.08), Cod (0.68 to 1.10), Saithe (0.33 to 1.36); mean shown above.
#> Simulation parameters:
#> Time period: 1967 to 2010
#> Output stored every 1 years
#> Time step Method:
summary(getEncounter(NS_params))
#> ℹ No `a` column so using a = 0.01 in w = a l^b, with w in g and l in cm.
#> ℹ No `b` column so using the isometric default b = 3 in w = a l^b.
#> ℹ No `a` column so using a = 0.01 in w = a l^b, with w in g and l in cm.
#> ℹ No `b` column so using the isometric default b = 3 in w = a l^b.
#> ℹ No `a` column so using a = 0.01 in w = a l^b, with w in g and l in cm.
#> ℹ No `b` column so using the isometric default b = 3 in w = a l^b.
#> ℹ No `a` column so using a = 0.01 in w = a l^b, with w in g and l in cm.
#> ℹ No `b` column so using the isometric default b = 3 in w = a l^b.
#> Encounter rate [g/year]
#> 12 species x 100 sizes
#>
#> Species Min Mean Max
#> Sprat 0.2992076 37.92979 239.6706
#> Sandeel 0.4528175 67.08030 434.6202
#> N.pout 0.5019776 237.84164 2613.4140
#> Herring 0.5752333 282.49597 2203.3821
#> Dab 0.4916095 395.38383 4473.2748
#> Whiting 0.4362525 2507.48128 28974.8976
#> Sole 0.3646753 474.92455 5616.2042
#> Gurnard 0.3122260 318.45335 3726.8753
#> Plaice 0.2323659 1898.07284 24800.9828
#> Haddock 0.5964130 3547.12149 49555.6495
#> Cod 0.9658343 52646.61006 436916.9135
#> Saithe 0.7709631 14646.02285 160506.0739
summary(getFMort(NS_sim))
#> Fishing mortality [1/year]
#> 44 times x 12 species x 100 sizes
#>
#> Species Min Mean Max
#> Sprat 0.0000000000 0.12879552 2.1827923
#> Sandeel 0.0000000000 0.11611087 1.3076336
#> N.pout 0.0000000000 0.13718398 2.1228994
#> Herring 0.0077894362 0.18281500 1.4419293
#> Dab 0.0017746971 0.02322755 0.1633433
#> Whiting 0.0126246154 0.17579846 1.3138868
#> Sole 0.0268320133 0.15939358 1.0259979
#> Gurnard 0.0000000000 0.00541050 0.1054407
#> Plaice 0.0089964121 0.18786555 0.8670560
#> Haddock 0.0015854377 0.22672527 1.4275500
#> Cod 0.0494762790 0.36512704 1.0721072
#> Saithe 0.0009633361 0.15208058 1.2032857
# }