Specify configuration for environmental covariates, effects on the population, and parameter values

Usage

set_ecov(input, ecov)

Arguments

input

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

ecov

(optional) named list of environmental covariate data and parameters (see details)

ecov specifies any environmental covariate data and model as well as the effect on the population. Environmental covariate data need not span the same years as the fisheries data. It can be NULL if no environmental data are to be fit. Otherwise, it must be a named list with the following components:

$label

Name(s) of the environmental covariate(s). Used in printing.

$mean

Mean observations (matrix). number of years x number of covariates. Missing values = NA.

$logsigma

Configure observation standard errors. Options:

Matrix of $log$ standard errors with same dimensions as $mean: Specified values for each time step
log standard errors for each covariate, numeric vector or matrix w/ dim 1 x n.ecov: Specified value the same for all time steps
estimation option (for all covariates). character string:: "est_1": Estimated, one value shared among time steps. "est_re": Estimated value for each time step as random effects with two parameters (mean, var)
list of two elements.: First is the matrix of log standard errors or the vector of single values for each covariate as above. Second is a character vector of estimation options (NA, "est_1","est_re") for each covariate. For covariates with non-NA values, values in the first element are ignored.

$year

Years corresponding to observations (vector of same length as $mean and $logsigma)

$use_obs

T/F (or 1/0) vector/matrix of the same dimension as $mean and $logsigma. Use the observation? Can be used to ignore subsets of the ecov without changing data files.

$process_model

Process model for the ecov time-series. "rw" = random walk, "ar1" = 1st order autoregressive, NA = do not fit

$process_mean_vals

vector of (initial) mean values for the ecov time-series.

$process_sig_vals

vector of (initial) standard deviation values for the ecov time-series.

$process_cor_vals

vector of (initial) correlation values for the ecov time-series.

$recruitment_how

character matrix (n_Ecov x n_stocks) indicating how each ecov affects recruitment for each stock. Options are based on (see Iles & Beverton (1998)) combined with the order of orthogonal polynomial of the covariate and has the form "type-lag-order". "type" can be:

= "none": no effect.
= "controlling": pre-recruit density-independent mortality.
= "limiting": maximum recruitment, e.g. ecov determines amount of suitable habitat)
= "lethal": threshold, i.e. R –> 0 at some ecov value.
= "masking": metabolic/growth, decreases dR/dS
= "directive": e.g. behavioral

for type other than "none", "lag" can be:

= "lag-n": lag = n which can be 0,1,2,.... lag-1 implies the covariate in year y affects recruitment in year y+1.

for "type" being other than "none", "order" can be:

= "linear": the covariate effect is linear on the transformed recruitment parameter (e.g., log).
= "poly-n": orthogonal polynomial where n = 1 (same as "linear"),2,...

so "limiting-lag-1-poly-2" would model the covariate affecting recruitment the next year (lag = 1) as a second order orthogonal polynomial to represent ($b_0 + b_1*\mathrm{Ecov} + b_2*\mathrm{Ecov} + ...$) limiting effect.

$M_how

character array (n_Ecov x n_stocks x n_ages x n_regions) indicating how each ecov affects M by age,stock,region and has the form "lag-order". "lag" can be:

= "none": no effect.
= "lag-n": lag = n which can be 0,1,2,.... lag-1 implies the covariate in year y affects M in year y+1.

for "lag" being other than "none", "order" can be:

= "linear": the covariate effect is linear on the transformed M parameter (e.g., log).
= "poly-n": orthogonal polynomial where n = 1 (same as "linear"),2,...

$M_effect_map

integer array (n_stocks x n_ages x n_regions x n_Ecov) indicating which estimated effects are common by age,stock,region. If not specified there the same effect is estimated for all M where $M_how is other than "none" for each covariate.

$q_how

character matrix (n_Ecov x n_indices) indicating whether each ecov affects catchability for each index. and has the form "lag-order". "lag" can be:

= "none": no effect.
= "lag-n": lag = n which can be 0,1,2,.... lag-1 implies the covariate in year y affects catchability in year y+1.

for "lag" being other than "none", "order" can be:

= "linear": the covariate effect is linear on the transformed catchability parameter (e.g., log).
= "poly-n": orthogonal polynomial where n = 1 (same as "linear"),2,...

$move_how

character array (n_Ecov x n_stocks x n_ages x n_seasons x n_regions x n_regions - 1) indicating whether each ecov affects movement from one region to the others by stock,age,season. and has the form "lag-order". "lag" can be:

= "none": no effect.
= "lag-n": lag = n which can be 0,1,2,.... lag-1 implies the covariate in year y affects a movement parameter in year y+1.

for "lag" being other than "none", "order" can be:

= "linear": the covariate effect is linear on the transformed movement parameter (e.g., log).
= "poly-n": orthogonal polynomial where n = 1 (same as "linear"),2,...

$move_effect_map

integer array (n_stocks x n_ages x n_seasons x n_regions x n_regions-1 x n_Ecov) indicating which estimated effects are common by age,stock,region, season etc. If not specified the same effect is estimated for all movement parameters where $move_how is other than "none" for each covariate.

$beta_R_vals

n_stocks x n_ecov x max(n_poly_R) array of initial values for effects on recruitment.

$beta_M_vals

n_stocks x n_ages x n_regions x n_ecov x max(n_poly_M) array of initial values for effects on natural mortality.

$beta_q_vals

n_indices x n_ecov x max(n_poly_q) array of initial values for effects on catchability.

$beta_mu_vals

n_stocks x n_ages x n_seasons x n_regions x n_regions - 1 x n_ecov x max(n_poly_move) array of initial values for effects on movement parameters.

Value

a named list with same elements as the input provided with environmental covariate observations, effects, and model 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"))
env.dat <- read.csv(file.path(path_to_examples,"GSI.csv"), header=T)
input <- prepare_wham_input(asap3, NAA_re = list(sigma = "rec"))
ecov <- list(
 label = "GSI",
 mean = as.matrix(env.dat$GSI),
 logsigma = 'est_1', # estimate obs sigma, 1 value shared across years
 year = env.dat$year,
 use_obs = matrix(1, ncol=1, nrow=dim(env.dat)[1]), # use all obs (=1)
 process_model = 'ar1') # "rw" or "ar1"
input <- set_ecov(input, ecov = ecov) #GSI in the model without any effects
} # }

Specify configuration for environmental covariates, effects on the population, and parameter values

Usage

Arguments

Value

See also

Examples