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 ~ 0 + depth_scaled + depth_scaled2,
  data = pcod_2011, mesh = pcod_mesh_2011, family = tweedie(),
  spatiotemporal = "AR1", time = "year")
head(spread_sims(m, nsim = 10))
#>   .iteration depth_scaled depth_scaled2    range      phi tweedie_p   ar1_rho
#> 1          1    -1.723420     -1.239501 59.20573 13.56981  1.535910 0.7677258
#> 2          2    -1.975947     -1.337013 51.06758 12.67182  1.573140 0.8312792
#> 3          3    -1.471611     -1.161968 68.90814 13.73527  1.525259 0.8790436
#> 4          4    -1.807872     -1.260145 67.47024 12.62994  1.563359 0.8887063
#> 5          5    -2.027082     -1.342981 62.47076 13.30531  1.564159 0.9195087
#> 6          6    -1.793759     -1.282912 53.66913 12.96081  1.549470 0.8483883
#>         sigma_O  sigma_E
#> 1           Inf 2.578915
#> 2  2.659516e-83 3.078770
#> 3  0.000000e+00 3.240282
#> 4 4.158774e-107 2.755055
#> 5  0.000000e+00 3.025966
#> 6 9.386198e-129 3.144829
head(gather_sims(m, nsim = 10))
#>   .iteration    .variable    .value
#> 1          1 depth_scaled -1.926309
#> 2          2 depth_scaled -1.625672
#> 3          3 depth_scaled -1.590939
#> 4          4 depth_scaled -1.979694
#> 5          5 depth_scaled -1.368575
#> 6          6 depth_scaled -2.177121
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`.
#> Warning: Removed 397 rows containing non-finite values (`stat_bin()`).