R/set_age_comp.R
set_age_comp.RdSpecify the age composition models for fleet(s) and indices.
set_age_comp(input, age_comp)list containing data, parameters, map, and random elements (output from wham::prepare_wham_input)
specifies the age composition models for fleet(s) and indices. If NULL, the multinomial is used because this was the only option in ASAP.
The age composition models available are:
"multinomial"Multinomial. This is the default because it was the only option in ASAP. 0 parameters.
"dir-mult"Saturating Dirichlet-multinomial, parameterized such that effective-sample-size is a nonlinear and saturating function with respect to input-sample-size. 1 parameter. Effective sample size is estimated by the model (Candy 2008)
"dirichlet-pool0"Dirichlet, pooling zero observations with adjacent age classes. 1. parameter. See Francis 2014 and Albertsen et al. 2016
"dirichlet-miss0""logistic-normal-miss0"Logistic normal, treating zero observations as missing. 1 parameter.
"logistic-normal-ar1-miss0"Logistic normal, treating zero observations as missing. 1 parameter.
"logistic-normal-pool0"Logistic normal, pooling zero observations with adjacent age classes. 1 parameter. See Schnute and Haigh (2007) and Francis (2014)
"logistic-normal-01-infl"Zero-or-one inflated logistic normal. Inspired by zero-one inflated beta in Ospina and Ferrari (2012). 3 parameters. . No OSA residuals.
"logistic-normal-01-infl-2par"Zero-one inflated logistic normal where p0 is a function of binomial sample size. 2 parameters. No OSA residuals.
"mvtweedie"Multivariate-tweedie, where the product of composition proportions and input sample sizes follows a distribution with mean equal to the product of predicted proportions and input sample size, and other parameters define the ratio of effective to input sample size (with is bounded 0 to Inf) and the probability of zeros. 2 parameters. No OSA residuals.
"dir-mult-linear"Linear Dirichlet-multinomial, parameterized such that effective-sample-size is a linear function with respect to input-sample-size, estimating 1 parameter, \(log(\theta)\), where the ratio of effective and input sample size is approximately \(\theta / (1+\theta)\), i.e., the logistic transformation of the estimated parameter \(log(\theta)\). (Thorson et al. 2017)
The two Dirichlet-multinomial options will only differ when input-sample-size differs among years. In these cases, the linear-Dirichlet multinomial is designed to decrease the effective sample size in each year by approximately the same proportion, while the saturating-Dirichlet multinomial will decrease the years with highest input-sample-size much more than those with lower input-sample-size.
One-step-ahead residuals will be calculated for all but options 8-10 when do.osa=TRUE (Nielsen et al. in prep.). An age composition model needs
to be specified for each fleet and index. If you would like all fleets and indices to use the same age composition likelihood, you
can simply specify one of the strings above, i.e. age_comp = "logistic-normal-miss0". If you do not want the same
age composition model to be used for all fleets and indices, you must specify a named list with the following entries:
A vector of the above strings with length = the number of fleets.
A vector of the above strings with length = the number of indices.
a named list with same elements as the input provided with age composition likelihood options modified.
if (FALSE) {
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)
input <- set_age_comp(input, age_comp = "logistic-normal-miss0") #no longer multinomial
}