Model parameters — mcp

Usage

mcp_pars(fit, scope = NULL, role = NULL)

Arguments

fit: An mcpfit object.
scope: Optional parameter scope(s): "population" or "group".
role: Optional parameter role(s), such as "fixed_effect", "dpar_effect", "arma", "group_sd", or "group_deviation".

Value

A data frame with one row per parameter definition. name is the model parameter name; dpar identifies its distributional parameter or component ("cp", "ar", or "ma"); order gives an AR/MA lag; group_col and population_name describe group-level effects.

Details

Return the canonical parameter definitions for an mcpfit object. This works before sampling and is the stable way to discover model parameters.

Examples

# Show every parameter in the model
mcp_pars(demo_fit)
#> # A tibble: 7 × 9
#>   name        part     scope role  segment dpar  order group_col population_name
#>   <chr>       <chr>    <chr> <chr>   <int> <chr> <int> <chr>     <chr>          
#> 1 cp_1        cp       popu… chan…       2 cp       NA NA        NA             
#> 2 cp_2        cp       popu… chan…       3 cp       NA NA        NA             
#> 3 Intercept_1 predict… popu… fixe…       1 mu       NA NA        NA             
#> 4 time_2      predict… popu… fixe…       2 mu       NA NA        NA             
#> 5 Intercept_3 predict… popu… fixe…       3 mu       NA NA        NA             
#> 6 time_3      predict… popu… fixe…       3 mu       NA NA        NA             
#> 7 sigma_1     predict… popu… dpar…       1 sigma    NA NA        NA             

# Select population-level coefficients
mcp_pars(demo_fit, scope = "population", role = "fixed_effect")
#> # A tibble: 4 × 9
#>   name        part     scope role  segment dpar  order group_col population_name
#>   <chr>       <chr>    <chr> <chr>   <int> <chr> <int> <chr>     <chr>          
#> 1 Intercept_1 predict… popu… fixe…       1 mu       NA NA        NA             
#> 2 time_2      predict… popu… fixe…       2 mu       NA NA        NA             
#> 3 Intercept_3 predict… popu… fixe…       3 mu       NA NA        NA             
#> 4 time_3      predict… popu… fixe…       3 mu       NA NA        NA