draws_df {posterior}

The draws_df format

Description

The as_draws_df() methods convert objects to the draws_df format. The draws_df() function creates an object of the draws_df format based on a set of numeric vectors. See Details.

Usage

as_draws_df(x, ...)

## Default S3 method:
as_draws_df(x, ...)

## S3 method for class 'data.frame'
as_draws_df(x, ...)

## S3 method for class 'draws_df'
as_draws_df(x, ...)

## S3 method for class 'draws_matrix'
as_draws_df(x, ...)

## S3 method for class 'draws_array'
as_draws_df(x, ...)

## S3 method for class 'draws_list'
as_draws_df(x, ...)

## S3 method for class 'draws_rvars'
as_draws_df(x, ...)

## S3 method for class 'mcmc'
as_draws_df(x, ...)

## S3 method for class 'mcmc.list'
as_draws_df(x, ...)

draws_df(..., .nchains = 1)

is_draws_df(x)

Arguments

x

An object to convert to a draws_df object.

...

For as_draws_df(): Arguments passed to individual methods (if applicable). For draws_df(): Named arguments containing numeric vectors each defining a separate variable.

.nchains

(positive integer) The number of chains. The default is 1.

Details

Objects of class "draws_df" are tibble data frames. They have one column per variable as well as additional metadata columns ".iteration", ".chain", and ".draw". The difference between the ".iteration" and ".draw" columns is that the former is relative to the MCMC chain while the latter ignores the chain information and has all unique values. See Examples.

If a data.frame-like object is supplied to as_draws_df that contains columns named ".iteration" or ".chain", they will be treated as iteration and chain indices, respectively. See Examples.

Value

A draws_df object, which has classes c("draws_df", "draws", class(tibble::tibble())).

See Also

Other formats: draws, draws_array(), draws_list(), draws_matrix(), draws_rvars()

Examples

x1 <- as_draws_df(example_draws())
class(x1)
print(x1)
str(x1)

x2 <- draws_df(a = rnorm(10), b = rnorm(10), c = 1)
class(x2)
print(x2)
str(x2)

# the difference between iteration and draw is clearer when contrasting
# the head and tail of the data frame
print(head(x1), reserved = TRUE, max_variables = 2)
print(tail(x1), reserved = TRUE, max_variables = 2)

# manually supply chain information
xnew <- data.frame(mu = rnorm(10), .chain = rep(1:2, each = 5))
xnew <- as_draws_df(xnew)
print(xnew)