View source: R/fidofit_methods.R
sample_prior.pibblefit | R Documentation |
Note this can be used to sample from prior and then predict can
be called to get counts or LambdaX (predict.pibblefit
)
## S3 method for class 'pibblefit'
sample_prior(
m,
n_samples = 2000L,
pars = c("Eta", "Lambda", "Sigma"),
use_names = TRUE,
...
)
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 |
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).
A pibblefit object
# Sample prior of already fitted pibblefit object
sim <- pibble_sim()
attach(sim)
fit <- pibble(Y, X)
head(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)
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