Bliss_Simulated_Annealing — Bliss_Simulated

A Simulated Annealing algorithm to compute the Bliss estimate.

Bliss_Simulated_Annealing(
  beta_sample,
  posterior_sample,
  param,
  verbose_cpp = FALSE
)

Arguments

beta_sample

a matrix. Each row is a coefficient function computed from the posterior sample.

posterior_sample

a list resulting from the Bliss_Gibbs_Sampler function.

param

a list containing:

grids: a list of numerical vectors, the qth vector is the grid of time points for the qth functional covariate.
basis: a character (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
burnin: an integer (optional), the number of iteration to drop from the posterior sample.
iter_sann: an integer (optional), the number of iteration of the Simulated Annealing algorithm.
k_max: an integer (optional), the maximal number of intervals for the Simulated Annealing algorithm.
l_max: an integer (optional), the maximal interval length for the Simulated Annealing algorithm.
Temp_init: a nonnegative value (optional), the initial temperature for the cooling function of the Simulated Annealing algorithm.
Q: an integer, the number of functional covariates.
p: a vector of integers, the numbers of time point of each functional covariate.
verbose: write stuff if TRUE (optional).

verbose_cpp

Rcpp writes stuff if TRUE (optional).

Value

a list containing:

Bliss_estimate: a numerical vector, corresponding to the Bliss estimate of the coefficient function.
Smooth_estimate: a numerical vector, which is the posterior expectation of the coefficient function for each time points.
trace: a matrix, the trace of the algorithm.

Examples

# \donttest{
data(data1)
data(param1)
data(res_bliss1)
param1$Q <- length(data1$x)
param1$grids <- data1$grids
param1$p <- sapply(data1$grids,length)

posterior_sample <- res_bliss1$posterior_sample
beta_sample <- compute_beta_sample(posterior_sample,param1)

res_sann <- Bliss_Simulated_Annealing(beta_sample,posterior_sample,param1)
# }