pgLM: Bayesian Polya-gamma regression
In jtipton25/pgR: Bayesian Polya-Gamma Regression

pgLM	R Documentation

Bayesian Polya-gamma regression

this function runs the Bayesian multinomial regression using Polya-gamma data augmentation

pgLM(
  Y,
  X,
  params,
  priors,
  n_cores = 1L,
  inits = NULL,
  config = NULL,
  n_chain = 1,
  sample_rmvn = FALSE
)

`Y`	is a n x J matrix of compositional count data.
`X`	is a n x p matrix of climate variables.
`params`	is a list of parameter settings. The list `params` must contain the following values: `n_adapt`: A positive integer number of adaptive MCMC iterations. `n_mcmc`: A positive integer number of total MCMC iterations post adaptation. `n_thin`: A positive integer number of MCMC iterations per saved sample. `n_message`: A positive integer number of frequency of iterations to output a progress message. For example, `n_message = 50` outputs progress messages every 50 iterations.
`priors`	is the list of prior settings.
`n_cores`	is the number of cores for parallel computation using openMP.
`inits`	is the list of intial values if the user wishes to specify initial values. If these values are not specified, then the intital values will be randomly sampled from the prior.
`config`	is the list of configuration values if the user wishes to specify initial values. If these values are not specified, then default a configuration will be used.
`n_chain`	is the MCMC chain id. The default is 1.
`sample_rmvn`	is an indicator whether the faster multivariate normal sampler is used.