Specify model and parameter configuration for numbers at age

Usage

set_NAA(input, NAA_re = NULL)

Arguments

input

list containing data, parameters, map, and random elements (output from prepare_wham_input)

NAA_re

(optional) list specifying options for numbers-at-age random effects, initial parameter values, and recruitment model (see details)

If NAA_re = NULL, a traditional statistical catch-at-age model is fit (NAA = pred_NAA for all ages, deterministic). Otherwise, NAA_re specifies numbers-at-age configuration. It is a list with the following possible entries:

$decouple_recruitment

T/F determining whether correlation structure of recruitment is independent of RE deviations for older ages (default = TRUE). Only applicable for NAA_re$sigma = "rec+1" and correlation across ages is specified. If TRUE and NAA_re$cor = "ar1_a", only deviations for ages>1 have the correlation structure. If TRUE and NAA_re$cor is "ar1_y" or "2dar1" separate year correlation parameters are estimated for recruitment and older ages.

$sigma

Which ages allow deviations from the predicted NAA given NAA from previous time step? Must be a single character string described below or a vector of length n_stocks. If length = 1, assumptions will be applied to all stocks. Options are specified with the strings:

"rec": Random effects on recruitment (deviations), all other ages deterministic
"rec+1": "Full state space" model with 2 estimated sigma_a, one for recruitment and one shared among other ages

$sigma_vals

Array (n_stocks x n_regions x n_ages) of initial standard deviation values to use for the NAA deviations. Values are not used if recruit_model = 1 and NAA_re$sigma is not specified. Only those for age 1 in the spawning region are used if NAA_re$sigma = "rec". If NAA_re$sigma_map is defined, the user must ensure that the configuration is compatible with NAA_re$sigma_vals

$sigma_map

You can specify a more complex parameter configuration by entering an integer array (nstocks x n_regions x n_ages), where each entry points to the NAA_sigma to use for that stock and age. E.g., for 2 stocks, 2 regions, and 6 ages, array(rep(c(1,2,2,3,3,3), each = 4),c(2,2,6)) will estimate 3 sigmas, with recruitment (age-1) deviations having their own sigma_R, ages 2-3 sharing sigma_2, and ages 4-6 sharing sigma_3. All parameters being the same for both stocks and across regions. The user must be sure that a compatible NAA_re$sigma configuration is defined. Values are not used if recruit_model = 1 and NAA_re$sigma is not specified.

$cor

Correlation structure for the NAA deviations. Must be a single character string described below or a vector of length n_stocks. If length = 1, assumptions will be applied to all stocks. Options are:

"iid": NAA deviations vary by year and age, but uncorrelated.
"ar1_a": NAA deviations correlated by age (AR1).
"ar1_y": NAA deviations correlated by year (AR1).
"2dar1": NAA deviations correlated by year and age (2D AR1).

$cor_vals

Array (n_stocks x n_regions x 3) of initial correlation values to use for the NAA deviations. The first value correspond to correlation across age. The second is for yearly correlation for NAA deviations for all ages if recruitment is not decoupled, or otherwise just ages after recruitemnt. The third correlation is for annual recruitment deviations and used only when recruitment is decoupled. If unspecified all initial values are 0. If NAA_re$cor = "iid", values are not used. If NAA_re$cor = "ar1_a", those for yearly correlation are not used, and vice versa for "ar1_y".

$cor_map

n_stocks x n_region matrix x 3 array of NA or integers indicating which random effects correlation parameters are estimated and whether to set any to be estimated identically. The 3 values for a given stock and region are for age, year ("survival"), and year ("recruitment"). Both age and years values are used for NAA_re$cor = "2dar1", but only the appropriate values are used for NAA_re$cor = "ar1_a", or "ar1_y". When NAA_re$decouple_recruitment = TRUE, the third value is used for both NAA_re$sigma = "rec" or "rec+1". For NAA_re$decouple_recruitment = FALSE, only the second value is used and applies to both recruitment and "survival" when NAA_re$sigma = "rec+1", and just recruitment when NAA_re$sigma = "rec". If not supplied stock-specific values for age and/or year will be estimated for all regions where NAA_re$cor is other than "none", "iid".

$N1_model

Character vector (n_stocks) determining which way to model the initial numbers at age:

"age-specific-fe": (default) age- and region-specific fixed effects parameters
"equilibrium": 2 fixed effects parameters: an initial recruitment and an instantaneous fishing mortality rate to generate an equilibruim abundance at age.
"iid-re": (default) age- and region-specific iid random effects parameters. 2 parameters: mean and sd for log NAA
"ar1-re": (default) age- and region-specific random effects parameters. 3 parameters: mean and sd, and cor for log NAA

$N1_pars

An (n_stocks x n_regions x n_ages) array. If N1_model = 0, then this should be filled with the initial values to use for abundance at age by stock and region in the first year. If N1_model = 1 (equilibrium assumption), only the first two values in the ages dimension are used: the (s,r,1) value is recruitment for stock (and region) and (s,r,2) is the fully-selected equilibrium fishing mortality rate generating the rest of the numbers at age in the first year.

$recruit_model

Integer vector (n_stocks) determining how to model recruitment. Overrides recruit_model argument to prepare_wham_input. Must make sure NAA_re$sigma, NAA_re$cor and ecov are properly specified.

1: SCAA, estimating annual recruitments as fixed effects.
2: estimating a mean recruitment with annual recruitments as random effects
3: Beverton-Holt stock-recruitment with annual recruitments as random effects
4: Ricker stock-recruitment with annual recruitments as random effects

$recruit_pars

list (length = n_stocks) of vectors of initial parameters for recruitment model. If $recruit_model is 3 or 4, parameters are "alpha" and "beta".

Value

a named list with same elements as the input provided with abundance modeling options modified.

Examples

if (FALSE) { # \dontrun{
wham.dir <- find.package("wham")
path_to_examples <- system.file("extdata", package="wham")
asap3 <- read_asap3_dat(file.path(path_to_examples,"ex1_SNEMAYT.dat"))
input <- prepare_wham_input(asap3)
NAA <- list(sigma = "rec")
input <- set_q(input, NAA_re = NAA) #estimate recruitment as random effects
} # }

Specify model and parameter configuration for numbers at age

Usage

Arguments

Value

See also

Examples