loo (version 2.0.0)

relative_eff: Convenience function for computing relative efficiencies


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.

data, draws, ...

Same as for the loo function method.


A vector of relative effective sample sizes.

Methods (by class)

  • 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.


Run this code
LLarr <- example_loglik_array()
LLmat <- example_loglik_matrix()

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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