Adjust Bayesian priors - One Source Trophic Position

Adjust priors for one source trophic position model derived from Post 2002.

Usage

one_source_priors_params(
  n1 = NULL,
  n1_sigma = NULL,
  dn = NULL,
  dn_sigma = NULL,
  tp_lb = NULL,
  tp_ub = NULL,
  sigma_lb = NULL,
  sigma_ub = NULL,
  bp = FALSE
)

Arguments

n1

mean (\(\mu\)) prior for the mean \(\delta^{15}\)N baseline. Defaults to 9.

n1_sigma

variance (\(\sigma\)) for the mean \(\delta^{15}\)N baseline. Defaults to 1.

dn

mean (\(\mu\)) prior value for \(\Delta\)N. Defaults to 3.4.

dn_sigma

variance (\(\sigma\)) for \(\delta^{15}\)N. Defaults to 0.25.

tp_lb

lower bound prior for trophic position. Defaults to 2.

tp_ub

upper bound prior for trophic position. Defaults to 10.

sigma_lb

lower bound prior for \(\sigma\). Defaults to 0.

sigma_ub

upper bound prior for \(\sigma\). Defaults to 10.

bp

logical value that controls whether informed priors are supplied to the model for \(\delta^{15}\)N baseline. Default is FALSE meaning the model will use uninformed priors, however, the supplied data.frame needs values for \(\delta^{15}\)N baseline (n1).

Value

stanvars object to be used with brms() call.

Details

$$\delta^{15}N = \delta^{15} N_1 + \delta N \times (tp - \lambda_1)$$

This function allows the user to adjust the priors for the following variables in the equation above:

• The mean (n1; \(\mu\)) and variance (n1_sigma; \(\sigma\)) for the mean \(\delta^{15}\)N for a given baseline (\(\delta^{15}N_1\)).This prior assumes a normal distribution.

• The mean (dn; \(\mu\)) and variance (dn_sigma; \(\sigma\)) of \(\Delta\)N (i.e, trophic enrichment factor). This prior assumes a normal distribution.

• The lower (tp_lb) and upper (tp_ub) bounds for trophic position. This prior assumes a uniform distribution.

• The lower (sigma_lb) and upper (sigma_ub) bounds for variance (\(\sigma\)). This prior assumes a uniform distribution.

See also

one_source_priors(), one_source_model(), and brms::brms()

Examples

one_source_priors_params()
#> $dn
#> $dn$name
#> [1] "dn"
#> 
#> $dn$sdata
#> [1] 3.4
#> 
#> $dn$scode
#> [1] "real dn;"
#> 
#> $dn$block
#> [1] "data"
#> 
#> $dn$position
#> [1] "start"
#> 
#> $dn$pll_args
#> [1] "data real dn"
#> 
#> 
#> $dn_sigma
#> $dn_sigma$name
#> [1] "dn_sigma"
#> 
#> $dn_sigma$sdata
#> [1] 0.25
#> 
#> $dn_sigma$scode
#> [1] "real dn_sigma;"
#> 
#> $dn_sigma$block
#> [1] "data"
#> 
#> $dn_sigma$position
#> [1] "start"
#> 
#> $dn_sigma$pll_args
#> [1] "data real dn_sigma"
#> 
#> 
#> $tp_lb
#> $tp_lb$name
#> [1] "tp_lb"
#> 
#> $tp_lb$sdata
#> [1] 2
#> 
#> $tp_lb$scode
#> [1] "real tp_lb;"
#> 
#> $tp_lb$block
#> [1] "data"
#> 
#> $tp_lb$position
#> [1] "start"
#> 
#> $tp_lb$pll_args
#> [1] "data real tp_lb"
#> 
#> 
#> $tp_ub
#> $tp_ub$name
#> [1] "tp_ub"
#> 
#> $tp_ub$sdata
#> [1] 10
#> 
#> $tp_ub$scode
#> [1] "real tp_ub;"
#> 
#> $tp_ub$block
#> [1] "data"
#> 
#> $tp_ub$position
#> [1] "start"
#> 
#> $tp_ub$pll_args
#> [1] "data real tp_ub"
#> 
#> 
#> $sigma_lb
#> $sigma_lb$name
#> [1] "sigma_lb"
#> 
#> $sigma_lb$sdata
#> [1] 0
#> 
#> $sigma_lb$scode
#> [1] "real sigma_lb;"
#> 
#> $sigma_lb$block
#> [1] "data"
#> 
#> $sigma_lb$position
#> [1] "start"
#> 
#> $sigma_lb$pll_args
#> [1] "data real sigma_lb"
#> 
#> 
#> $sigma_ub
#> $sigma_ub$name
#> [1] "sigma_ub"
#> 
#> $sigma_ub$sdata
#> [1] 10
#> 
#> $sigma_ub$scode
#> [1] "real sigma_ub;"
#> 
#> $sigma_ub$block
#> [1] "data"
#> 
#> $sigma_ub$position
#> [1] "start"
#> 
#> $sigma_ub$pll_args
#> [1] "data real sigma_ub"
#> 
#> 
#> attr(,"class")
#> [1] "stanvars"