Set prior(s) for a network model

set_prior(x, prior, param = "", use_regexp = TRUE, quiet = FALSE)

set_priors(x, prior, param = "", use_regexp = TRUE, quiet = FALSE)

Arguments

x: A networkModel object.
prior: A prior built with e.g. uniform_p() or hcauchy_p(). Call available_priors() to see a table of implemented priors. Alternatively, if prior is a tibble, the function will try to use it to set parameter priors. The format of such an argument is the same as the format of the output of the getter function priors() (see examples). Note that if `prior` is given as a tibble, all other arguments (except `x`) are disregarded.
param: String, target parameter or regexp to target several parameters. Default is the empty string "", which will match all parameters.
use_regexp: Boolean, if TRUE (the default) then param is used as a regular expression to match one or several parameter names.
quiet: Boolean, if FALSE print a message indicating which parameters had their prior modified.

Value

A networkModel object.

Examples

# Copy `aquarium_mod`
m <- aquarium_mod
priors(m)
#> # A tibble: 8 × 2
#>   in_model                 prior                       
#>   <chr>                    <list>                      
#> 1 eta                      <trun_normal(mean=0,sd=0.5)>
#> 2 lambda_algae             <trun_normal(mean=0,sd=0.2)>
#> 3 lambda_daphnia           <trun_normal(mean=0,sd=0.2)>
#> 4 lambda_NH4               <trun_normal(mean=0,sd=0.2)>
#> 5 upsilon_algae_to_daphnia <trun_normal(mean=0,sd=0.5)>
#> 6 upsilon_daphnia_to_NH4   <trun_normal(mean=0,sd=0.5)>
#> 7 upsilon_NH4_to_algae     <trun_normal(mean=0,sd=0.5)>
#> 8 zeta                     <trun_normal(mean=0,sd=0.5)>

# Modify the priors of `m`
m <- set_priors(m, exponential_p(0.5), "lambda")
#> Prior modified for parameter(s): 
#>   - lambda_algae
#>   - lambda_daphnia
#>   - lambda_NH4
priors(m)
#> # A tibble: 8 × 2
#>   in_model                 prior                       
#>   <chr>                    <list>                      
#> 1 eta                      <trun_normal(mean=0,sd=0.5)>
#> 2 lambda_algae             <exponential(lambda=0.5)>   
#> 3 lambda_daphnia           <exponential(lambda=0.5)>   
#> 4 lambda_NH4               <exponential(lambda=0.5)>   
#> 5 upsilon_algae_to_daphnia <trun_normal(mean=0,sd=0.5)>
#> 6 upsilon_daphnia_to_NH4   <trun_normal(mean=0,sd=0.5)>
#> 7 upsilon_NH4_to_algae     <trun_normal(mean=0,sd=0.5)>
#> 8 zeta                     <trun_normal(mean=0,sd=0.5)>

# Re-apply priors from the original `aquarium_mod`
prev_priors <- priors(aquarium_mod)
prev_priors
#> # A tibble: 8 × 2
#>   in_model                 prior                       
#>   <chr>                    <list>                      
#> 1 eta                      <trun_normal(mean=0,sd=0.5)>
#> 2 lambda_algae             <trun_normal(mean=0,sd=0.2)>
#> 3 lambda_daphnia           <trun_normal(mean=0,sd=0.2)>
#> 4 lambda_NH4               <trun_normal(mean=0,sd=0.2)>
#> 5 upsilon_algae_to_daphnia <trun_normal(mean=0,sd=0.5)>
#> 6 upsilon_daphnia_to_NH4   <trun_normal(mean=0,sd=0.5)>
#> 7 upsilon_NH4_to_algae     <trun_normal(mean=0,sd=0.5)>
#> 8 zeta                     <trun_normal(mean=0,sd=0.5)>
m <- set_priors(m, prev_priors)
#> Prior modified for parameter(s): 
#>   - eta
#>   - lambda_algae
#>   - lambda_daphnia
#>   - lambda_NH4
#>   - upsilon_algae_to_daphnia
#>   - upsilon_daphnia_to_NH4
#>   - upsilon_NH4_to_algae
#>   - zeta
priors(m)
#> # A tibble: 8 × 2
#>   in_model                 prior                       
#>   <chr>                    <list>                      
#> 1 eta                      <trun_normal(mean=0,sd=0.5)>
#> 2 lambda_algae             <trun_normal(mean=0,sd=0.2)>
#> 3 lambda_daphnia           <trun_normal(mean=0,sd=0.2)>
#> 4 lambda_NH4               <trun_normal(mean=0,sd=0.2)>
#> 5 upsilon_algae_to_daphnia <trun_normal(mean=0,sd=0.5)>
#> 6 upsilon_daphnia_to_NH4   <trun_normal(mean=0,sd=0.5)>
#> 7 upsilon_NH4_to_algae     <trun_normal(mean=0,sd=0.5)>
#> 8 zeta                     <trun_normal(mean=0,sd=0.5)>