`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)

x

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)`

).

chain_id

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`

.

cores

The number of cores to use for parallelization.

data, draws, ...

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`i`

th 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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