Get parameter estimates and model estimates

Usage

par_est(mod)

Arguments

mod: Fitted model object from run_mod

Value

List with model estimates (pred_summary) and parameter estimates (par_summary)

Examples

dat <- sim_slr(n_sim = 30)
mod <- run_mod(dat, model = "slr")
#> module glm loaded
#> Compiling model graph
#>    Resolving undeclared variables
#>    Allocating nodes
#> Graph information:
#>    Observed stochastic nodes: 60
#>    Unobserved stochastic nodes: 33
#>    Total graph size: 380
#> 
#> Initializing model
#> 
#> No convergence issues detected
par_est(mod)
#> $pred_summary
#> # A tibble: 50 × 4
#>        x pred_y lwr_95 upr_95
#>    <dbl>  <dbl>  <dbl>  <dbl>
#>  1 0.136  0.195 0.0944  0.296
#>  2 0.174  0.233 0.137   0.331
#>  3 0.212  0.271 0.179   0.365
#>  4 0.250  0.308 0.218   0.400
#>  5 0.287  0.346 0.260   0.433
#>  6 0.325  0.384 0.303   0.467
#>  7 0.363  0.422 0.344   0.502
#>  8 0.401  0.459 0.385   0.539
#>  9 0.439  0.497 0.425   0.574
#> 10 0.477  0.535 0.467   0.608
#> # … with 40 more rows
#> 
#> $par_summary
#> # A tibble: 2 × 16
#>   variable   mean median     sd    mad      q5   q95  rhat ess_bulk ess_tail
#>   <chr>     <dbl>  <dbl>  <dbl>  <dbl>   <dbl> <dbl> <dbl>    <dbl>    <dbl>
#> 1 alpha    0.0613 0.0601 0.0561 0.0548 -0.0311 0.153 0.999     948.     866.
#> 2 beta     0.996  0.995  0.0518 0.0505  0.912  1.09  1.00      901.     851.
#> # … with 6 more variables: par_mean <dbl>, par_median <dbl>, par_sd <dbl>,
#> #   par_mad <dbl>, par_q5 <dbl>, par_q95 <dbl>
#>