relative_eff() computes the the MCMC effective sample size divided by
the total sample size.
relative_eff(x, ...)# S3 method for default
relative_eff(x, chain_id, ...)
# S3 method for matrix
relative_eff(x, chain_id, ..., cores = getOption("mc.cores", 1))
# S3 method for array
relative_eff(x, ..., cores = getOption("mc.cores", 1))
# S3 method for `function`
relative_eff(
x,
chain_id,
...,
cores = getOption("mc.cores", 1),
data = NULL,
draws = NULL
)
# S3 method for importance_sampling
relative_eff(x, ...)
A vector of relative effective sample sizes.
A vector, matrix, 3-D array, or function. See the Methods (by
class) section below for details on specifying x, but where
"log-likelihood" is mentioned replace it with one of the following
depending on the use case:
For use with the loo() function, the values in x (or generated by
x, if a function) should be likelihood values
(i.e., exp(log_lik)), not on the log scale.
For generic use with psis(), the values in x should be the reciprocal
of the importance ratios (i.e., exp(-log_ratios)).
A vector of length NROW(x) containing MCMC chain
indexes for each each row of x (if a matrix) or each value in
x (if a vector). No chain_id is needed if x is a 3-D
array. If there are C chains then valid chain indexes are values
in 1:C.
The number of cores to use for parallelization.
Same as for the loo() function method.
relative_eff(default): A vector of length \(S\) (posterior sample size).
relative_eff(matrix): An \(S\) by \(N\) matrix, where \(S\) is the size
of the posterior sample (with all chains merged) and \(N\) is the number
of data points.
relative_eff(array): An \(I\) by \(C\) by \(N\) array, where \(I\)
is the number of MCMC iterations per chain, \(C\) is the number of
chains, and \(N\) is the number of data points.
relative_eff(`function`): A function f() that takes arguments data_i and draws and returns a
vector containing the log-likelihood for a single observation i evaluated
at each posterior draw. The function should be written such that, for each
observation i in 1:N, evaluating
f(data_i = data[i,, drop=FALSE], draws = draws)
results in a vector of length S (size of posterior sample). The
log-likelihood function can also have additional arguments but data_i and
draws are required.
If using the function method then the arguments data and draws must also
be specified in the call to loo():
data: A data frame or matrix containing the data (e.g.
observed outcome and predictors) needed to compute the pointwise
log-likelihood. For each observation i, the ith row of
data will be passed to the data_i argument of the
log-likelihood function.
draws: An object containing the posterior draws for any
parameters needed to compute the pointwise log-likelihood. Unlike
data, which is indexed by observation, for each observation the
entire object draws will be passed to the draws argument of
the log-likelihood function.
The ... can be used if your log-likelihood function takes additional
arguments. These arguments are used like the draws argument in that they
are recycled for each observation.
relative_eff(importance_sampling): If x is an object of class "psis", relative_eff() simply returns
the r_eff attribute of x.
LLarr <- example_loglik_array()
LLmat <- example_loglik_matrix()
dim(LLarr)
dim(LLmat)
rel_n_eff_1 <- relative_eff(exp(LLarr))
rel_n_eff_2 <- relative_eff(exp(LLmat), chain_id = rep(1:2, each = 500))
all.equal(rel_n_eff_1, rel_n_eff_2)
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