Extract parameter simulations from the joint precision matrix

spread_sims() returns a wide-format data frame. gather_sims() returns a long-format data frame. The format matches the format in the tidybayes spread_draws() and gather_draws() functions.

Usage

spread_sims(object, nsim = 200, n_sims = deprecated())

gather_sims(object, nsim = 200, n_sims = deprecated())

Arguments

object

Output from sdmTMB().

nsim

The number of simulation draws.

n_sims

Deprecated: please use nsim.

Value

A data frame. gather_sims() returns a long-format data frame:

• .iteration: the sample ID

• .variable: the parameter name

• .value: the parameter sample value

spread_sims() returns a wide-format data frame:

• .iteration: the sample ID

• columns for each parameter with a sample per row

Examples

m <- sdmTMB(density ~ depth_scaled,
  data = pcod_2011, mesh = pcod_mesh_2011, family = tweedie())
head(spread_sims(m, nsim = 10))
#>   .iteration X.Intercept. depth_scaled    range      phi tweedie_p  sigma_O
#> 1          1     3.749276   -0.5134865 30.20537 14.48993  1.583609 1.836304
#> 2          2     2.437613   -0.8824028 18.67602 14.36332  1.577593 2.124812
#> 3          3     2.643799   -0.6484220 40.34146 14.89017  1.609824 2.481647
#> 4          4     1.720072   -0.6486199 13.61455 15.09968  1.620128 2.860899
#> 5          5     2.557104   -0.6799320 31.43043 14.85466  1.591829 2.126323
#> 6          6     2.713461   -0.8159928 13.68706 15.25929  1.596875 2.902257
head(gather_sims(m, nsim = 10))
#>   .iteration    .variable   .value
#> 1          1 X.Intercept. 3.296844
#> 2          2 X.Intercept. 2.336084
#> 3          3 X.Intercept. 2.407328
#> 4          4 X.Intercept. 2.344606
#> 5          5 X.Intercept. 2.959705
#> 6          6 X.Intercept. 3.163177
samps <- gather_sims(m, nsim = 1000)

if (require("ggplot2", quietly = TRUE)) {
  ggplot(samps, aes(.value)) + geom_histogram() +
    facet_wrap(~.variable, scales = "free_x")
}
#> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.