binomial_control: Set computational options for the sampling algorithms
In mcmcsae: Markov Chain Monte Carlo Small Area Estimation

binomial_control

R Documentation

Set computational options for the sampling algorithms

Set computational options for the sampling algorithms

binomial_control(PG.approx = TRUE, PG.approx.m = -2L, probit.HaarPXDA = TRUE)

`PG.approx`	whether Polya-Gamma draws for logistic binomial models are approximated by a hybrid gamma convolution approach. If not, `BayesLogit::rpg` is used, which is exact for some values of the shape parameter.
`PG.approx.m`	if `PG.approx=TRUE`, the number of explicit gamma draws in the sum-of-gammas representation of the Polya-Gamma distribution. The remainder (infinite) convolution is approximated by a single moment-matching gamma draw. Special values are: `-2L` for a default choice depending on the value of the shape parameter balancing performance and accuracy, `-1L` for a moment-matching normal approximation, and `0L` for a moment-matching gamma approximation.
`probit.HaarPXDA`	only used for binomial models with probit link. If supported, `probit.HaarPXDA=TRUE` and the Haar PX-DA sandwich step is added to the Albert-Chib data augmentation scheme for probit multilevel models. This will usually result in a faster mixing MCMC algorithm. Currently supported when all coefficients are sampled in a single Gibbs block and all coefficients' prior means equal zero.