This function is largely a more user friendly wrapper around
optimPibbleCollapsed
and
uncollapsePibble
.
See details for model specification.
Notation: N
is number of samples,
D
is number of multinomial categories, Q
is number
of covariates, iter
is the number of samples of eta
(e.g.,
the parameter n_samples
in the function
optimPibbleCollapsed
)
D x N matrix of counts (if NULL uses priors only)
Q x N matrix of covariates (design matrix) (if NULL uses priors only, must be present to sample Eta)
dof for inverse wishart prior (numeric must be > D) (default: D+3)
(D-1) x Q matrix of prior mean for regression parameters (default: matrix(0, D-1, Q))
QxQ prior covariance matrix (default: diag(Q))
(D-1)x(D-1) prior covariance matrix (default: ALR transform of diag(1)*(upsilon-D)/2 - this is essentially iid on "base scale" using Aitchison terminology)
(D-1) x Q initialization for Eta for optimization
character vector of posterior parameters to return
arguments passed to optimPibbleCollapsed
and
uncollapsePibble
object of class pibblefit
an object of class pibblefit
the full model is given by: $$Y_j \sim Multinomial(Pi_j)$$ $$Pi_j = Phi^{-1}(Eta_j)$$ $$Eta \sim MN_{D-1 x N}(Lambda*X, Sigma, I_N)$$ $$Lambda \sim MN_{D-1 x Q}(Theta, Sigma, Gamma)$$ $$Sigma \sim InvWish(upsilon, Xi)$$ Where Gamma is a Q x Q covariance matrix, and Phi^-1 is ALRInv_D transform.
Default behavior is to use MAP estimate for uncollaping the LTP model if laplace approximation is not preformed.
JD Silverman K Roche, ZC Holmes, LA David, S Mukherjee. Bayesian Multinomial Logistic Normal Models through Marginally Latent Matrix-T Processes. 2019, arXiv e-prints, arXiv:1903.11695
fido_transforms
provide convenience methods for
transforming the representation of pibblefit objects (e.g., conversion to
proportions, alr, clr, or ilr coordinates.)
access_dims
provides convenience methods for accessing
dimensions of pibblefit object
Generic functions including summary
,
print
,
coef
,
as.list
,
predict
,
name
, and
sample_prior
name_dims
Plotting functions provided by plot
and ppc
(posterior predictive checks)
sim <- pibble_sim()
fit <- pibble(sim$Y, sim$X)