as_draws_matrix.projection | R Documentation |

`draws_matrix`

(see package
posterior)These are the `posterior::as_draws()`

and `posterior::as_draws_matrix()`

methods for `projection`

objects (returned by `project()`

, possibly as
elements of a `list`

). They extract the projected parameter draws and return
them as a `draws_matrix`

. In case of different (i.e., nonconstant) weights
for the projected draws, a `draws_matrix`

allows for a safer handling of
these weights (safer in contrast to the matrix returned by
`as.matrix.projection()`

), in particular by providing the natural input for
`posterior::resample_draws()`

(see section "Examples" below).

```
## S3 method for class 'projection'
as_draws_matrix(x, ...)
## S3 method for class 'projection'
as_draws(x, ...)
```

`x` |
An object of class |

`...` |
Arguments passed to |

In case of the augmented-data projection for a multilevel submodel
of a `brms::categorical()`

reference model, the multilevel parameters (and
therefore also their names) slightly differ from those in the brms
reference model fit (see section "Augmented-data projection" in
`extend_family()`

's documentation).

An `S_{\mathrm{prj}} \times Q`

`draws_matrix`

(see
`posterior::draws_matrix()`

) of projected draws, with
`S_{\mathrm{prj}}`

denoting the number of projected draws and
`Q`

the number of parameters. If the projected draws have nonconstant
weights, `posterior::weight_draws()`

is applied internally.

```
# Data:
dat_gauss <- data.frame(y = df_gaussian$y, df_gaussian$x)
# The "stanreg" fit which will be used as the reference model (with small
# values for `chains` and `iter`, but only for technical reasons in this
# example; this is not recommended in general):
fit <- rstanarm::stan_glm(
y ~ X1 + X2 + X3 + X4 + X5, family = gaussian(), data = dat_gauss,
QR = TRUE, chains = 2, iter = 500, refresh = 0, seed = 9876
)
# Projection onto an arbitrary combination of predictor terms (with a small
# value for `nclusters`, but only for illustrative purposes; this is not
# recommended in general):
prj <- project(fit, solution_terms = c("X1", "X3", "X5"), nclusters = 5,
seed = 9182)
# Applying the posterior::as_draws_matrix() generic to the output of
# project() dispatches to the projpred::as_draws_matrix.projection()
# method:
prj_draws <- posterior::as_draws_matrix(prj)
# Resample the projected draws according to their weights:
set.seed(3456)
prj_draws_resampled <- posterior::resample_draws(prj_draws, ndraws = 1000)
# The values from the following two objects should be the same (in general,
# this only holds approximately):
print(proportions(table(rownames(prj_draws_resampled))))
print(weights(prj_draws))
# Treat the resampled draws like ordinary draws, e.g., summarize them:
print(posterior::summarize_draws(
prj_draws_resampled,
"median", "mad", function(x) quantile(x, probs = c(0.025, 0.975))
))
# Or visualize them using the `bayesplot` package:
if (requireNamespace("bayesplot", quietly = TRUE)) {
print(bayesplot::mcmc_intervals(prj_draws_resampled))
}
```

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