fit_Bliss {bliss}  R Documentation 
Fit the Bayesian Functional Linear Regression model (with Q functional covariates).
fit_Bliss(data, param, compute_density = TRUE, sann = TRUE, verbose = FALSE)
data 
a list containing:

param 
a list containing:

compute_density 
a logical value. If TRUE, the posterior density of the coefficient function is computed. (optional) 
sann 
a logical value. If TRUE, the Bliss estimate is computed with a Simulated Annealing Algorithm. (optional) 
verbose 
write stuff if TRUE (optional). 
return a list containing:
a list of Q numerical vector. Each vector is the function alpha(t) associated to a functional covariate. For each t, alpha(t) is the posterior probabilities of the event "the support covers t".
a list of Q items. Each item contains a list
containing information to plot the posterior density of the
coefficient function with the image
function.
grid_t
a numerical vector: the xaxis.
grid_beta_t
a numerical vector: the yaxis.
density
a matrix: the z values.
new_beta_sample
a matrix: beta sample used to compute the posterior densities.
a list of Q matrices. The qth matrix is a posterior sample of the qth functional covariates.
a list of numerical vectors corresponding to the Bliss estimates of each functional covariates.
a list of posterior_sample
. chains
is NULL
if
n_chains
=1.
a list for each chain providing: a mu estimate, a sigma_sq estimate, the Smooth estimate of the coefficient function and the autocorrelation of the Markov Chain.
a list containing the data.
a list of information about the posterior sample: the trace matrix of the Gibbs sampler, a list of Gibbs sampler parameters and the posterior densities.
a list of support estimates of each functional covariate.
another version of the support estimates.
a list of Q matrices which are the trace of the Simulated Annealing algorithm.
# see the vignette BlissIntro.