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)
A vector, matrix, 3-D array, or function. See the Methods
(by class) section below for details on the shape of x. For use
with the loo function, the values in x (or generated by
x if x is 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.
A vector of relative effective sample sizes.
default: A vector of length \(S\) (posterior sample size).
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.
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.
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 to pass additional arguments to your
log-likelihood function. These arguments are used like the draws
argument in that they are recycled for each observation.
# NOT RUN {
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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