posterior (version 1.6.0)

for_each_draw: Loop over draws

Description

Executes an expression once for every draw in a draws object. Used primarily for its side effects and returns the input x invisibly.

Usage

for_each_draw(x, expr)

Value

As for_each_draw() is used primarily for its side effects (the expression executed for each draw of x), it returns the input x invisibly.

Arguments

x

(draws) A draws object or another R object for which the method is defined.

expr

(expression) A bare expression that can contain references to variables in x by name. This expression will be executed once per draw of x, where references to variables in x resolve to the value of that variable in that draw. The expression supports quasiquotation.

Details

If x is not in the draws_rvars format, it is first converted to that format. This allows the variables in x to include their dimensions (i.e, to act as R vectors and arrays) when being referred to in expr.

Within expr, use .draw to refer to the draw index, which will be a value between 1 and ndraws(x). expr is executed in the calling environment of for_each_draw(), so it can use variables in that environment (however, due to the use of data masking, to modify variables in that environment, one must use <<-.)

Examples

eight_schools <- as_draws_rvars(example_draws())


# 1. A simple example --- looping over draws and printing each draw
# NOTE: You probably don't want to do this in practice! This example is
# just intended to show what for_each_draw() is doing. If you just want to
# print the draws of an rvar, it is probably better to use draws_of()
for_each_draw(eight_schools, {
  print(mu)
})


# 2. A more complex example --- building a parallel coordinates plot
# First, construct the plot bounds
plot(1, type = "n",
  xlim = c(1, length(eight_schools$theta)),
  ylim = range(range(eight_schools$theta)),
  xlab = "school", ylab = "theta"
)

# Then, use for_each_draw() to make a parallel coordinates plot of all draws
# of eight_schools$theta. Use resample_draws(eight_schools, n = ...)
# in place of eight_schools if a smaller sample is desired for the plot.
for_each_draw(eight_schools, {
  lines(seq_along(theta), theta, col = rgb(1, 0, 0, 0.05))
})

# Finally, add means and 90% intervals
lines(seq_along(eight_schools$theta), mean(eight_schools$theta))
with(summarise_draws(eight_schools$theta),
  segments(seq_along(eight_schools$theta), y0 = q5, y1 = q95)
)