Fit the Bayesian Functional Linear Regression model (with Q functional covariates).
fit_Bliss(data, param, compute_density = TRUE, sann = TRUE,
verbose = FALSE)a list containing:
an integer, the number of functional covariates.
a numerical vector, the outcomes.
a list of matrices, the qth matrix contains the observations of the
qth functional covariate at time points given by grids.
a list of numerical vectors, the qth vector is the grid of time points for the qth functional covariate.
a list containing:
an integer, the number of iterations of the Gibbs sampler algorithm.
a vector of integers, corresponding to the numbers of intervals for each covariate.
a character vector (optional). The possible values are "uniform" (default), "epanechnikov", "gauss" and "triangular" which correspond to different basis functions to expand the coefficient function and the functional covariates
an integer (optional), the number of iteration to drop from the posterior sample.
an integer (optional), the number of iteration of the Simulated Annealing algorithm.
an integer (optional), the maximal number of intervals for the Simulated Annealing algorithm.
an integer (optional), the maximal interval length for the Simulated Annealing algorithm.
an integer (optional), correspond to the lims option
of the kde2d funtion.
an integer (optional) which corresponds to the number of Gibbs sampler runs.
a list of Q vectors (optional) to compute beta samples on different grids.
a nonnegative value (optional), the initial temperature for the cooling function of the Simulated Annealing algorithm.
an integer (optional) to thin the posterior sample.
an integer (optional), the number of times the algorithm will be executed
a logical value. If TRUE, the posterior density of the coefficient function is computed. (optional)
a logical value. If TRUE, the Bliss estimate is computed with a Simulated Annealing Algorithm. (optional)
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_ta numerical vector: the x-axis.
grid_beta_ta numerical vector: the y-axis.
densitya matrix: the z values.
new_beta_samplea 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.
# NOT RUN {
# see the vignette BlissIntro.
# }
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