sample_prior.pibblefit: Sample from the prior distribution of pibblefit object

In jsilve24/mongrel: Bayesian Multinomial Logistic Normal Regression

Description

Note this can be used to sample from prior and then predict can be called to get counts or LambdaX (predict.pibblefit)

Usage

## S3 method for class 'pibblefit'
sample_prior(m, n_samples = 2000L, pars = c("Eta",
  "Lambda", "Sigma"), use_names = TRUE, ...)

Arguments

m	object of class pibblefit
n_samples	number of samples to produce
pars	parameters to sample
use_names	should names be used if available
...	currently ignored

Details

Could be greatly speed up in the future if needed by sampling directly from cholesky form of inverse wishart (currently implemented as header in this library - see MatDist.h).

Examples

# Sample prior of already fitted  pibblefit object
sim <- pibble_sim()
attach(sim)
fit <- pibble(Y, X)
sample_prior(fit)

# Sample prior as part of model fitting
m <- pibblefit(N=as.integer(sim$N), D=as.integer(sim$D), Q=as.integer(sim$Q), 
                iter=2000L, upsilon=upsilon, 
                Xi=Xi, Gamma=Gamma, Theta=Theta, X=X, 
                coord_system="alr", alr_base=D)
m <- sample_prior(m)
plot(m) # plot prior distribution (defaults to parameter Lambda) 

jsilve24/mongrel documentation built on Jan. 27, 2022, 9:54 p.m.