params.RdShortcuts to parameterise a model with g3_param
g3_parameterized(
name,
by_stock = FALSE,
by_predator = FALSE,
by_year = FALSE,
by_step = FALSE,
by_age = FALSE,
by_area = FALSE,
exponentiate = FALSE,
avoid_zero = FALSE,
scale = 1,
offset = 0,
ifmissing = NULL,
...)Suffix for parameter name.
Should there be individual parameters per-stock?
No
Produce a "stock_name.name" parameter
Select the stock name_part(s) to use, e.g. to produce "stock_species.name" parameter with "species"
g3_stock objectsProduce a parameter that applies to all given stocks
Should there be individual parameters per-predator (read: per-fleet) stock?
No
Produce a "fleet_stock_name.name" parameter
Select the stock name_part(s) to use, e.g. to produce "fleet_country.name" parameter with "country"
g3_stock objectsProduce a parameter that applies to all given stocks
Should there be individual parameters per model year?
No
Produce a "name.1998" parameter for each year the model runs
Override the year range, so when projecting there will be sufficient parameters available.
Should there be individual parameters per step within years?
No
Produce a "name.1" seasonal parameter for each step, or "name.1998.1" for every timestep in the model if combined with by_year.
Should there be individual parameters per stock age?
No
Produce a "name.4" parameter for each age of the stock(s) in by_stock
Should there be individual parameters per stock area?
No
Produce a "name.area" parameter for each area of the stock(s) in by_stock
Value to use for when there is no valid parameter (read: year when by_year = TRUE) Either a numeric constant or character. If character, add another parameter for ifmissing, using the same by_stock value.
Use exp(value) instead of the raw parameter value. Will add "_exp" to the parameter name.
If TRUE, wrap parameter with avoid_zero
Use scale * value instead of the raw parameter value.
Either a numeric constant or character.
If character, add another parameter for scale, using the same by_stock value.
Use value + offset instead of the raw parameter value
Either a numeric constant or character.
If character, add another parameter for offset, using the same by_stock value.
Additional parameters passed through to g3_param, e.g. optimise, random, ...
The function provides shortcuts to common formulas used when parameterising a model.
A formula object defining the given parameters
stock_imm <- g3_stock(c(species = 'stock', 'imm'), seq(10, 35, 5)) %>% g3s_age(1, 4)
stock_mat <- g3_stock(c(species = 'stock', 'mat'), seq(10, 35, 5)) %>% g3s_age(3, 6)
# Helper function that shows the parameter template for the given parameter
param_template_for <- function (g3_param) {
model_code <- g3_to_tmb(list(
# g3a_naturalmortality() isn't important, it is a place to add our parameter
g3a_naturalmortality(stock_imm, g3_param),
# We also need stock_mat in the model at least once
g3a_naturalmortality(stock_mat, 0),
# Set a year range to use for parameters where relevant
g3a_time(1990, 1994) ))
# Extract template, throw away default parameters from g3a_time()
params <- attr(model_code, "parameter_template")
params <- params[!(rownames(params) %in% c('retro_years', 'project_years')),]
return(params)
}
# Not 'by' anything, so we add a single parameter value
param_template_for( g3_parameterized('K') )
#> switch type value optimise random lower upper parscale source
#> K K 0 TRUE FALSE NA NA NA g3a_naturalmortality
# Can set defaults for the parameter template when defining a parameter
param_template_for( g3_parameterized('K', value = 5, lower = 2,
upper = 8, optimise = FALSE) )
#> switch type value optimise random lower upper parscale source
#> K K 5 FALSE FALSE 2 8 NA g3a_naturalmortality
# by_stock, so the parameters will have the stock name prepended
param_template_for( g3_parameterized('K', by_stock = TRUE) )
#> switch type value optimise random lower upper parscale
#> stock_imm.K stock_imm.K 0 TRUE FALSE NA NA NA
#> source
#> stock_imm.K g3a_naturalmortality
# Similarly, we can prepend year & age
param_template_for( g3_parameterized('K', by_stock = TRUE, by_year = TRUE, by_age = TRUE) )
#> switch type value optimise random lower upper
#> stock_imm.K.1990.1 stock_imm.K.1990.1 0 TRUE FALSE NA NA
#> stock_imm.K.1991.1 stock_imm.K.1991.1 0 TRUE FALSE NA NA
#> stock_imm.K.1992.1 stock_imm.K.1992.1 0 TRUE FALSE NA NA
#> stock_imm.K.1993.1 stock_imm.K.1993.1 0 TRUE FALSE NA NA
#> stock_imm.K.1994.1 stock_imm.K.1994.1 0 TRUE FALSE NA NA
#> stock_imm.K.1990.2 stock_imm.K.1990.2 0 TRUE FALSE NA NA
#> stock_imm.K.1991.2 stock_imm.K.1991.2 0 TRUE FALSE NA NA
#> stock_imm.K.1992.2 stock_imm.K.1992.2 0 TRUE FALSE NA NA
#> stock_imm.K.1993.2 stock_imm.K.1993.2 0 TRUE FALSE NA NA
#> stock_imm.K.1994.2 stock_imm.K.1994.2 0 TRUE FALSE NA NA
#> stock_imm.K.1990.3 stock_imm.K.1990.3 0 TRUE FALSE NA NA
#> stock_imm.K.1991.3 stock_imm.K.1991.3 0 TRUE FALSE NA NA
#> stock_imm.K.1992.3 stock_imm.K.1992.3 0 TRUE FALSE NA NA
#> stock_imm.K.1993.3 stock_imm.K.1993.3 0 TRUE FALSE NA NA
#> stock_imm.K.1994.3 stock_imm.K.1994.3 0 TRUE FALSE NA NA
#> stock_imm.K.1990.4 stock_imm.K.1990.4 0 TRUE FALSE NA NA
#> stock_imm.K.1991.4 stock_imm.K.1991.4 0 TRUE FALSE NA NA
#> stock_imm.K.1992.4 stock_imm.K.1992.4 0 TRUE FALSE NA NA
#> stock_imm.K.1993.4 stock_imm.K.1993.4 0 TRUE FALSE NA NA
#> stock_imm.K.1994.4 stock_imm.K.1994.4 0 TRUE FALSE NA NA
#> parscale source
#> stock_imm.K.1990.1 NA g3a_naturalmortality
#> stock_imm.K.1991.1 NA g3a_naturalmortality
#> stock_imm.K.1992.1 NA g3a_naturalmortality
#> stock_imm.K.1993.1 NA g3a_naturalmortality
#> stock_imm.K.1994.1 NA g3a_naturalmortality
#> stock_imm.K.1990.2 NA g3a_naturalmortality
#> stock_imm.K.1991.2 NA g3a_naturalmortality
#> stock_imm.K.1992.2 NA g3a_naturalmortality
#> stock_imm.K.1993.2 NA g3a_naturalmortality
#> stock_imm.K.1994.2 NA g3a_naturalmortality
#> stock_imm.K.1990.3 NA g3a_naturalmortality
#> stock_imm.K.1991.3 NA g3a_naturalmortality
#> stock_imm.K.1992.3 NA g3a_naturalmortality
#> stock_imm.K.1993.3 NA g3a_naturalmortality
#> stock_imm.K.1994.3 NA g3a_naturalmortality
#> stock_imm.K.1990.4 NA g3a_naturalmortality
#> stock_imm.K.1991.4 NA g3a_naturalmortality
#> stock_imm.K.1992.4 NA g3a_naturalmortality
#> stock_imm.K.1993.4 NA g3a_naturalmortality
#> stock_imm.K.1994.4 NA g3a_naturalmortality
# You can specify the name part to use,
# e.g. if a parameter should be shared between mature and immature:
param_template_for( g3_parameterized('K', by_stock = 'species', by_year = TRUE) )
#> switch type value optimise random lower upper parscale
#> stock.K.1990 stock.K.1990 0 TRUE FALSE NA NA NA
#> stock.K.1991 stock.K.1991 0 TRUE FALSE NA NA NA
#> stock.K.1992 stock.K.1992 0 TRUE FALSE NA NA NA
#> stock.K.1993 stock.K.1993 0 TRUE FALSE NA NA NA
#> stock.K.1994 stock.K.1994 0 TRUE FALSE NA NA NA
#> source
#> stock.K.1990 g3a_naturalmortality
#> stock.K.1991 g3a_naturalmortality
#> stock.K.1992 g3a_naturalmortality
#> stock.K.1993 g3a_naturalmortality
#> stock.K.1994 g3a_naturalmortality
# Can give a list of stocks, in which case it works out name parts for you
param_template_for( g3_parameterized('K', by_stock = list(stock_imm, stock_mat)) )
#> switch type value optimise random lower upper parscale
#> stock.K stock.K 0 TRUE FALSE NA NA NA
#> source
#> stock.K g3a_naturalmortality
param_template_for( g3_parameterized('K', by_stock = list(stock_imm, stock_mat), by_age = TRUE) )
#> switch type value optimise random lower upper parscale
#> stock.K.1 stock.K.1 0 TRUE FALSE NA NA NA
#> stock.K.2 stock.K.2 0 TRUE FALSE NA NA NA
#> stock.K.3 stock.K.3 0 TRUE FALSE NA NA NA
#> stock.K.4 stock.K.4 0 TRUE FALSE NA NA NA
#> stock.K.5 stock.K.5 0 TRUE FALSE NA NA NA
#> stock.K.6 stock.K.6 0 TRUE FALSE NA NA NA
#> source
#> stock.K.1 g3a_naturalmortality
#> stock.K.2 g3a_naturalmortality
#> stock.K.3 g3a_naturalmortality
#> stock.K.4 g3a_naturalmortality
#> stock.K.5 g3a_naturalmortality
#> stock.K.6 g3a_naturalmortality
# If there are no shared name parts, then all names will be added
param_template_for( g3_parameterized(
'btrigger',
by_stock = list(g3_fleet("surv"), g3_fleet("comm"))) )
#> switch type value optimise random lower upper
#> comm.surv.btrigger comm.surv.btrigger 0 TRUE FALSE NA NA
#> parscale source
#> comm.surv.btrigger NA g3a_naturalmortality
# You can set fixed scale/offset for the parameter
g3_parameterized('K', scale = 5, offset = 9)
#> g3_param("K") * 5 + 9
# ...or give names and they will also be parameters, sharing the by_stock setting
param_template_for( g3_parameterized('K', by_stock = TRUE, scale = "sc", offset = "offs") )
#> switch type value optimise random lower upper parscale
#> stock_imm.K stock_imm.K 0 TRUE FALSE NA NA NA
#> stock_imm.sc stock_imm.sc 0 TRUE FALSE NA NA NA
#> stock_imm.offs stock_imm.offs 0 TRUE FALSE NA NA NA
#> source
#> stock_imm.K g3a_naturalmortality
#> stock_imm.sc g3a_naturalmortality
#> stock_imm.offs g3a_naturalmortality