Specify model and parameter configuration for selectivity

Specify model and parameter configuration for selectivity

Usage

set_selectivity(input, selectivity)

Arguments

input

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

selectivity

(optional) list specifying options for selectivity blocks, models, initial parameter values, parameter fixing/mapping, and random effects (see details)

set_selectivity specifies options for selectivity and allows you to overwrite existing options in the input list or as specified in the ASAP data file. If selectivity = NULL, selectivity options from input are used.

prepare_wham_input(..., selectivity=selectivity) calls set_selectivity(..., selectivity=selectivity). If you already have created input with prepare_wham_input, you can also use set_selectivity(input, selectivity=selectivity) to modify the selectivity specification.

selectivity is a list with the following entries:

$model

Selectivity model for each block. Vector with length = number of selectivity blocks. Each entry must be one of: "age-specific", "logistic", "double-logistic", or "decreasing-logistic".

$re

Time-varying (random effects) for each block. Vector with length = number of selectivity blocks. If NULL, selectivity parameters in all blocks are constant over time and uncorrelated. Each entry of selectivity$re must be one of the following options, where the selectivity parameters are:

"none"

(default) are constant and uncorrelated

"iid"

vary by year and age/par, but uncorrelated

"ar1"

correlated by age/par (AR1), but not year

"ar1_y"

correlated by year (AR1), but not age/par

"2dar1"

correlated by year and age/par (2D AR1)

$initial_pars

Initial parameter values for each block. List of length = number of selectivity blocks. Each entry must be a vector of length # parameters in the block, i.e. c(2,0.2) for logistic (a50 and 1/slope) or c(0.5,0.5,0.5,1,1,0.5) for age-specific parameters when there are 6 ages. Default is to set at middle of parameter range. This is 0.5 for age-specific and n.ages/2 or logistic, double-logistic, and decreasing-logistic.

$fix_pars

Alternative to $map_pars for specifying which selectivity parameters (only fixed effects) to fix at initial values. List of length = number of selectivity blocks. E.g. model with 3 age-specific blocks and 6 ages, list(4:5, 4, 2:4)) will fix ages 4 and 5 in block 1, age 4 in block 2, and ages 2, 3, and 4 in block 3. Use NULL to not fix any parameters for a block, e.g. list(NULL, 4, 2) does not fix any pars in block 1.

$par_min

The lower bound for selectivity parameters and is used to populate data$selpars_lower. List of length = number of selectivity blocks, where each item is a vector of length = number of selectivity parameters (age-specific: n.ages, logistic: 2, double-logistic: 4).

$par_max

The upper bound for selectivity parameters and is used to populate data$selpars_upper. List of length = number of selectivity blocks, where each item is a vector of length = number of selectivity parameters (age-specific: n.ages, logistic: 2, double-logistic: 4).

$map_pars

Alternative to $fix_pars for specifying how to fix selectivity parameters (only fixed effects), corresponds to map$logit_selpars. List of length = number of selectivity blocks, where each item is a vector of length = number of selectivity parameters (age-specific: n.ages, logistic: 2, double-logistic: 4). Use NA to fix a parameter and integers to estimate. Use the same integer for multiple ages or fleets/indices to estimate a shared parameter. E.g. for a model with 3 age-specific blocks (1 fleet, 2 indices) and 6 ages, $map_pars = list(c(1,2,3,NA,NA,4), c(5,6,7,NA,8,8), c(1,2,3,NA,NA,4)) will estimate ages 1-3 and 6 in block 1 (fleet), ages 1-3 and 4-5 (shared) in block 2 (index 1), and then set the index 2 (block 3) selectivity equal to the fleet.

$sigma_vals

Initial standard deviation values to use for the random effect deviations. Must be a vector with length = number of blocks. Use natural (not log) scale, must be positive. par$sel_repars[,1] will be estimated on log-scale. Not used if re = 'none' for all blocks.

$map_sigma

Specify which SD parameters to fix for the random effect deviations. Must be a vector with length = number of blocks. Use NA to fix a parameter and integers to estimate. Use the same integer for multiple blocks to estimate a shared SD parameter. Not used if re = 'none' for all blocks.

$cor_vals

Initial correlation values to use for the random effect deviations. Must be a n_selblocks x 2 integer matrix. Columns correspond to correlation by age and year, respectively. If re = 'ar1' or re = 'ar1_y' only the corresponding values are used. Values must be between -1 and 1, but parameters are estimated on a logit transformed scale internally. Not used if re = 'none' or re = 'iid' for all blocks.

$map_cor

Specify which correlation parameters to fix for the random effect deviations. Must be a n_selblocks x 2 matrix. Columns correspond to correlation by age and year,respectively. Parameters can be shared by setting corresponding values of map_cor to the same integer. Use NA to fix a parameter. If re = 'ar1' or re = 'ar1_y', only the column for the corresponding correlation are used. Not used if re = 'none' or re = 'iid' for all blocks.

$n_selblocks

How many selectivity blocks. Optional. If unspecified and no asap3 object, then this is set to the number of fleets + indices. If specified, ensure other components of selectivity are consistent.

Value

a named list with same elements as the input provided with selectivity 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, NAA_re = list(sigma = "rec"))
sel <- list(model=rep("logistic",input$data$n_selblocks),
   initial_pars=rep(list(c(3,3)),input$data$n_selblocks),
   fix_pars=rep(list(NULL),input$data$n_selblocks))
input <- set_selectivity(input, selectivity = sel) #logistic selectivity for all selectivity blocks
} # }