prior: Priors for MSA model

In multiSA: Multi-Stock Assessment

Priors for MSA model

Description

Priors in MSA are set by providing character strings which are then parsed into an expression and evaluated in the model environment (see example). This provides flexibility to set a prior for any desired model parameter or variable. See list of parameters in the documentation for ⁠[check_parameters()]⁠ for options (note that priors for log_rdev_ys and log_initrdev_as are not needed as they're hard-coded into the model). Several functions below generate the character string for the prior for important dynamics parameters, such as natural mortality and steepness.

Usage

prior_h(MSAdata, s = 1, m, stdev)

prior_M(MSAdata, s = 1, meanlog, sdlog)

prior_q(MSAdata, i = 1, meanlog, sdlog)

Arguments

MSAdata	Data object. Class MSAdata
s	Integer for stock
m	Mean in un-transformed space
stdev	Standard deviation in un-transformed space
meanlog	Mean of the lognormal distribution on the log scale
sdlog	Standard of the lognormal distribution on the log scale
i	Integer for the corresponding index

Details

• prior_h returns the log prior for stock-recruit steepness. Beta distribution for the Beverton-Holt function and normal distribution for Ricker function.

• prior_M returns the log prior for natural mortality. Lognormal distribution.

• prior_q returns the log prior for index catchability. Lognormal distribution.

Value

Character.

Examples

# Add M and steepness prior to model

dat <- new("MSAdata")
dat@Dmodel@ns <- 1
dat@Dstock@SRR_s <- "BH"

pr_M <- prior_M(dat, s = 1, log(0.25), 0.3)
pr_h <- prior_h(dat, s = 1, 0.8, 0.15)
dat@Dmodel@prior <- c(pr_M, pr_h)

multiSA documentation built on March 21, 2026, 1:06 a.m.