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Bayesian Mixture of Experts Linear Regression.

bmoe.Rd

Fits the model in JAGS.

Usage

bmoe(object, ..., prior, jags_n = bmoe_jags_n(), inits = NULL)

# S3 method for formula
bmoe(object, data, ..., prior, jags_n = bmoe_jags_n(), inits = NULL)

# S3 method for bmoe_sim
bmoe(object, ..., prior, jags_n = bmoe_jags_n(), inits = NULL)

Arguments

object

object. A formula or simulated object.

...

These dots are for future extensions and must be empty.

prior

named list. See Prior section.

jags_n

named list. See JAGS Controls section.

inits

list. Passed to rjags::jags.model().

data

data frame. To be used in modelling.

Prior

Hyper-parameters that must be passed to the prior argument are in bold.

  • k, number of components, assumed known.

  • regr is element-wise IID Normal with 0 mean and regr_prec precision.

  • wt is element-wise IID Normal with 0 mean and wt_prec precision.

  • prec is element-wise IID Gamma with prec_shape and prec_rate.

JAGS Controls

  • n.adapt controls number of discarded samples in adaptation stage.

  • n.update controls number of discarded samples in warm-up stage.

  • n.iter controls how many samples are saved.

  • n.thin controls thinning, where only every \(n^th\) sample is kept.

  • n.chains controls number of chains.

See also

example_bmoe_fit().