as_draws_matrix.projection function

Extract projected parameter draws and coerce to 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, ...)

Arguments

• x: An object of class projection (returned by project(), possibly as elements of a list).
• ...: Arguments passed to as.matrix.projection(), except for allow_nonconst_wdraws_prj.

Returns

An $S_prj x Q$ draws_matrix (see posterior::draws_matrix()) of projected draws, with $S_prj$ 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.

Details

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).

Examples

# 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, predictor_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))
}