View source: R/set_compute_options.R
set_compute_options | R Documentation |
Set parameters related to the Metropolis-Hastings algorithm.
set_compute_options(
nmc = 2000,
burnin = NULL,
alpha_prop_sd = 0.1,
rho_proposal = c("ls", "swap"),
leap_size = 1,
aug_method = c("uniform", "pseudo"),
pseudo_aug_metric = c("footrule", "spearman"),
swap_leap = 1,
alpha_jump = 1,
aug_thinning = 1,
clus_thinning = 1,
rho_thinning = 1,
include_wcd = FALSE,
save_aug = FALSE,
save_ind_clus = FALSE
)
nmc |
Integer specifying the number of iteration of the
Metropolis-Hastings algorithm to run. Defaults to |
burnin |
Integer defining the number of samples to discard. Defaults to
|
alpha_prop_sd |
Numeric value specifying the |
rho_proposal |
Character string specifying the proposal distribution of
modal ranking |
leap_size |
Integer specifying the step size of the distribution defined
in |
aug_method |
Augmentation proposal for use with missing data. One of "pseudo" and "uniform". Defaults to "uniform", which means that new augmented rankings are proposed by sampling uniformly from the set of available ranks, see Section 4 in \insertCitevitelli2018;textualBayesMallows. Setting the argument to "pseudo" instead, means that the pseudo-likelihood proposal defined in Chapter 5 of \insertCitesteinSequentialInferenceMallows2023;textualBayesMallows is used instead. |
pseudo_aug_metric |
String defining the metric to be used in the
pseudo-likelihood proposal. Only used if |
swap_leap |
Integer specifying the leap size for the swap proposal used
for proposing latent ranks in the case of non-transitive pairwise
preference data. Note that leap size for the swap proposal when used for
proposal the modal ranking |
alpha_jump |
Integer specifying how many times to sample |
aug_thinning |
Integer specifying the thinning for saving augmented
data. Only used when |
clus_thinning |
Integer specifying the thinning to be applied to cluster
assignments and cluster probabilities. Defaults to |
rho_thinning |
Integer specifying the thinning of |
include_wcd |
Logical indicating whether to store the within-cluster
distances computed during the Metropolis-Hastings algorithm. Defaults to
|
save_aug |
Logical specifying whether or not to save the augmented
rankings every |
save_ind_clus |
Whether or not to save the individual cluster
probabilities in each step. This results in csv files |
An object of class "BayesMallowsComputeOptions"
, to be provided in
the compute_options
argument to compute_mallows()
,
compute_mallows_mixtures()
, or update_mallows()
.
Other preprocessing:
get_transitive_closure()
,
set_initial_values()
,
set_model_options()
,
set_priors()
,
set_progress_report()
,
set_smc_options()
,
setup_rank_data()
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