Adds draws from the posterior distribution of model to the data object
using one of fitted_samples(), predicted_samples(), or
posterior_samples().
Usage
add_fitted_samples(
data,
model,
...,
n_draws = NULL,
seed = NULL,
chains = NULL
)
add_predicted_samples(data, model, ..., n_draws = NULL, seed = NULL)
add_posterior_samples(
data,
model,
...,
n_draws = NULL,
seed = NULL,
chains = NULL
)
add_smooth_samples(
data,
model,
...,
n_draws = NULL,
seed = NULL,
select = NULL,
chains = NULL
)Arguments
- data
a data frame or tibble to which the posterior draws will be added.
- model
a fitted GAM (or GAM-like) object for which a posterior draw method exists.
- ...
arguments are passed to the posterior draw function, currently one of
fitted_samples(),predicted_samples(), orposterior_samples().n_drawsandseedare already specified here as arguments and are also passed on to the posterior sampling function.- n_draws
positive integer; total retained draws across all chains. Required when generating draws. With
method = "user", the supplied matrix determines the count; an explicit count must agree with its rows.- seed
numeric; a value to seed the random number generator.
- chains
positive integer or
NULL; number of chains.NULLuses one chain, or the chain mapping in supplied draws.n_drawsmust be divisible bychains. Burn-in is applied separately to each MH chain.- select
character; select which smooth's posterior to draw from. The default,
NULL, means the posteriors of all smooths in model will be returned using the same joint coefficient draws. If supplied, a vector of smooth terms.
Examples
load_mgcv()
df <- data_sim("eg1", n = 400, seed = 42)
m <- gam(y ~ s(x0) + s(x1) + s(x2) + s(x3), data = df, method = "REML")
# add fitted samples (posterior draws of the expected value of the response)
# note that there are 800 rows in the output: 400 data by `n_draws = 2` samples.
df |>
add_fitted_samples(m, n_draws = 2, seed = 84)
#> # A tibble: 800 × 14
#> y x0 x1 x2 x3 f f0 f1 f2 f3 .row .draw
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <int> <int>
#> 1 2.99 0.915 0.0227 0.909 0.402 1.62 0.529 1.05 0.0397 0 1 1
#> 2 2.99 0.915 0.0227 0.909 0.402 1.62 0.529 1.05 0.0397 0 1 2
#> 3 4.70 0.937 0.513 0.900 0.432 3.25 0.393 2.79 0.0630 0 2 1
#> 4 4.70 0.937 0.513 0.900 0.432 3.25 0.393 2.79 0.0630 0 2 2
#> 5 13.9 0.286 0.631 0.192 0.664 13.5 1.57 3.53 8.41 0 3 1
#> 6 13.9 0.286 0.631 0.192 0.664 13.5 1.57 3.53 8.41 0 3 2
#> 7 5.71 0.830 0.419 0.532 0.182 6.12 1.02 2.31 2.79 0 4 1
#> 8 5.71 0.830 0.419 0.532 0.182 6.12 1.02 2.31 2.79 0 4 2
#> 9 7.63 0.642 0.879 0.522 0.838 10.4 1.80 5.80 2.76 0 5 1
#> 10 7.63 0.642 0.879 0.522 0.838 10.4 1.80 5.80 2.76 0 5 2
#> # ℹ 790 more rows
#> # ℹ 2 more variables: .parameter <chr>, .fitted <dbl>
# add posterior draws from smooth s(x2)
df |>
add_smooth_samples(m, n_draws = 2, seed = 2, select= "s(x2)")
#> # A tibble: 800 × 15
#> y x0 x1 x2 x3 f f0 f1 f2 f3 .row .smooth
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <int> <chr>
#> 1 2.99 0.915 0.0227 0.909 0.402 1.62 0.529 1.05 0.0397 0 1 s(x2)
#> 2 2.99 0.915 0.0227 0.909 0.402 1.62 0.529 1.05 0.0397 0 1 s(x2)
#> 3 4.70 0.937 0.513 0.900 0.432 3.25 0.393 2.79 0.0630 0 2 s(x2)
#> 4 4.70 0.937 0.513 0.900 0.432 3.25 0.393 2.79 0.0630 0 2 s(x2)
#> 5 13.9 0.286 0.631 0.192 0.664 13.5 1.57 3.53 8.41 0 3 s(x2)
#> 6 13.9 0.286 0.631 0.192 0.664 13.5 1.57 3.53 8.41 0 3 s(x2)
#> 7 5.71 0.830 0.419 0.532 0.182 6.12 1.02 2.31 2.79 0 4 s(x2)
#> 8 5.71 0.830 0.419 0.532 0.182 6.12 1.02 2.31 2.79 0 4 s(x2)
#> 9 7.63 0.642 0.879 0.522 0.838 10.4 1.80 5.80 2.76 0 5 s(x2)
#> 10 7.63 0.642 0.879 0.522 0.838 10.4 1.80 5.80 2.76 0 5 s(x2)
#> # ℹ 790 more rows
#> # ℹ 3 more variables: .term <chr>, .draw <int>, .value <dbl>