![[Experimental]](figures/lifecycle-experimental.svg)
Arguments
- fit
An
mcpfitobject.- scope
Optional parameter scope(s):
"population"or"group".- role
Optional parameter role(s):
"change_point","coefficient"(regression coefficients of all distributional parameters, e.g.,mu,sigma, andar1), or"group_sd". Combine withscopeto select population-level parameters or their group-level deviations, e.g.,scope = "group", role = "change_point"for group-level change points.
Value
A tibble with one row per parameter definition. parameter is the
model parameter name; dpar is its distributional parameter (e.g., "mu",
"sigma", or "ar1"; see mcp-formula), as accepted by the dpar argument of
fitted.mcpfit(), fixef.mcpfit(), etc., and NA for change points;
group_col and population_parameter describe group-level effects.
Details
Return the canonical parameter definitions for an mcpfit object. This
works before sampling, so you can discover the model parameters before setting priors or inits.
Examples
# Show every parameter in the model
print(mcp_pars(demo_fit), width = Inf)
#> # A tibble: 7 × 8
#> parameter part scope role segment dpar group_col population_parameter
#> <chr> <chr> <chr> <chr> <int> <chr> <chr> <chr>
#> 1 cp_1 cp population change_point 2 NA NA NA
#> 2 cp_2 cp population change_point 3 NA NA NA
#> 3 Intercept_1 predictor population coefficient 1 mu NA NA
#> 4 time_2 predictor population coefficient 2 mu NA NA
#> 5 Intercept_3 predictor population coefficient 3 mu NA NA
#> 6 time_3 predictor population coefficient 3 mu NA NA
#> 7 sigma_1 predictor population coefficient 1 sigma NA NA
# Select population-level coefficients
mcp_pars(demo_fit, scope = "population", role = "coefficient")$parameter
#> [1] "Intercept_1" "time_2" "Intercept_3" "time_3" "sigma_1"