Sample from the prior distribution of pibblefit object

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

Value

A pibblefit object

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)
#> The following object is masked from package:fido:
#> 
#>     Y
fit <- pibble(Y, X)
head(sample_prior(fit))
#> $D
#> [1] 10
#> 
#> $N
#> [1] 30
#> 
#> $Q
#> [1] 2
#> 
#> $iter
#> [1] 2000
#> 
#> $coord_system
#> [1] "alr"
#> 
#> $alr_base
#> [1] 10
#> 

# 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) 
#> Scale for colour is already present.
#> Adding another scale for colour, which will replace the existing scale.