Specify model and parameter configuration for natural mortality

Specify model and parameter configuration for natural mortality

Usage

set_M(input, M)

Arguments

input

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

M

(optional) list specifying natural mortality options: model, random effects, initial values, and parameters to fix (see details)

M specifies estimation options for natural mortality and can overwrite M-at-age values specified in the ASAP data file. If NULL, the M-at-age matrix from the ASAP data file is used (M fixed, not estimated). To estimate M-at-age shared/mirrored among some but not all ages, modify M$means_map (see vignette for more details). M is a list with the following entries:

$mean_model

Character describing the type of model for M stock and regional models for natural mortality. Options are:

"fixed-M"

Use initial values from ASAP3 dat files or $initial_means for (mean) M as fixed values. If no ASAP3 files and $initial_means is not provided, default is M = 0.2 for all stocks, regions and ages

"estimate-M"

estimate one or more (mean) M parameters. Default is to estimate a single M shared across all stocks and ages, but use $means_map to fix or estimate parameters for specific stocks, regions, ages.

"weight-at-age"

specifies M as a function of weight-at-age, \(M_{y,a} = \mathrm{exp}(b0 + b1*\mathrm{log}(W_{y,a}))\), as in Lorenzen (1996) and Miller & Hyun (2018). Default is to estimate a single model shared across all stocks and regions, but use $means_map[s,r,1] to fix or estimate the intercept for specific stocks, regions. See also $logb_prior and $initial_b configuring the slope on log scale.

$initial_means

array (n_stocks x n_regions x n_ages) of initial/mean M by stock, region and age. If NULL, initial mean M-at-age values for a given stock and region are taken from the first row of the MAA matrix in the ASAP data file. If no ASAP data file, M = 0.2 is the default. If $mean_model is "weight-at-age" only elements for the first age ($initial_means[,,1]) are used (for the intercept of log(M)).

$means_map

array (n_stocks x n_regions x n_ages) of NA or integers ( 0 <= max <= n_stocks * n_regions * n_ages) indicating which ages to estimate (mean) M and whether to set any ages to be identical. E.g. in a model with 2 stock, 2 regions and 6 ages with constant M estimated for each stock across regions and ages $M_ages_map[1,,] = 1 and $M_ages_map[2,,] = 2. $M_ages_map[1,1,] = c(NA,1,1,2,2,3) will fix M for age 1 at the initial value, and estimates for ages 2 and 3 are identical as are those for ages 4 and 5 and different from age 6+ for stock 1 and region 1. If NULL, specifies all ages fixed at M$initial_means. If $mean_model is "weight-at-age" these are used for all stocks and regions and only the elements for the first age ($M_ages_map[,,1]) are used (for the intercept of log(M)).

$intial_MAA

array (n_stocks x n_regions x n_years x n_ages) of initial values for M at age. Intended to be uses when nothing pertaining to M estimated.

$b_model

"constant","stock","region", "stock-region" defining whether parameter is constant, stock-specific, region-specific, stock- and region-specific. Only used if M$mean_model = "weight-at-age".

$b_prior

T/F, should a N(mu, 0.08) prior (where mu = log(0.305) by default) be used on log_b? Based on Fig. 1 and Table 1 (marine fish) in Lorenzen (1996). (Only used if $mean_model is "weight-at-age").

$intial_b

if any elements of $mean_model is "weight-at-age", initial value for mean b for weight-at-age model.

$re_model

Character matrix (n_stocks x n_regions) of options for time- and age-varying (random effects) on M by stock and region. Possible values are:

"none"

(default) No random effects by age or year.

"iid_a"

uncorrelated M by age, constant in time.

"iid_y"

uncorrelated M by year, constant all ages.

"ar1_a"

M correlated by age (AR1), constant in time.

"ar1_y"

M correlated by year (AR1), constant all ages.

"iid_ay"

M uncorrelated by year and age (2D).

"ar1_ay"

M correlated by year and age (2D AR1), as in Cadigan (2016).

$re_map

array (n_stocks x n_regions x n_ages) of NA and integers (1 <= max <= n_ages) indicating which ages, for a given stock and region, have random effects (not NA) and whether to set RE for any ages to be identical. E.g. in a model with 2 stock, 2 regions and 6 ages, $re_map[2,1,] = c(NA,1,1,2,2,3) will not estimate RE for age 1, and those for ages 2 and 3 are identical as are those for ages 4 and 5 and different from age 6+ for stock 2 and region 1. If NULL, and $re_model specifies M random effects at age, at least two ages must be specified for correlation among ages to be estimated.

$sigma_vals

n_stocks x n_regions matrix Initial standard deviation value to use for the M random effects. Values are not used if M$re_model = "none". Otherwise, a single value. If unspecified all values are 0.1.

$cor_vals

n_stocks x n_regions x 2 array of initial correlation values to use for the M deviations. If unspecified all initial values are 0. When M$re_model =

"iid_a", "iid_y", "iid_ay" or "none"

values are not used.

"ar1_a"

first value cor_vals[s,r,1] is used.

"ar1_y"

second value cor_vals[s,r,2] is used.

"ar1_ay"

First is for "age", second is for "year".

$sigma_map

n_stocks x n_region matrix of NA or integers indicating which random effects sd is estimated and whether to set any to be identical. If not supplied a single sd will be estimated for any stock and region where $re_model is other than "none".

$cor_map

n_stocks x n_region matrix x 2 array of NA or integers indicating which random effects correlation parameters are estimated and whether to set any to be identical. If not supplied a single value for age and/or year will be estimated for any stock and region where $re_model is other than "none", "iid_a", "iid_y".

Value

a named list with same elements as the input provided with natural mortality options modified.

See also

prepare_wham_input

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)
M = list(mean_model = "estimate-M")
input <- set_M(input, M = M) #estimate a constant M parameters
} # }