Nothing
## ----setup, include = FALSE---------------------------------------------------
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
## ----echo=TRUE,message=FALSE, warning=FALSE-----------------------------------
library(bliss)
## ----eval=TRUE,include = TRUE-------------------------------------------------
set.seed(1)
param <- list( # define the "param" to simulate data
Q=1, # the number of functional covariate
n=100, # n is the sample size and p is the
p=c(50), # number of time observations of the curves
beta_types=c("smooth"), # define the shape of the "true" coefficient function
grids_lim=list(c(0,1))) # Give the beginning and the end of the observation's domain of the functions.
data <- sim(param) # Simulate the data
## ----eval=TRUE, include = TRUE------------------------------------------------
param <- list( # define the required values of the Bliss method.
iter=1e3, # The number of iteration of the main numerical algorithm of Bliss.
burnin=2e2, # The number of burnin iteration for the Gibbs Sampler
K=c(3)) # The number of intervals of the beta
res_bliss<-fit_Bliss(data=data,param=param,verbose=TRUE)
# Structure of a Bliss object
str(res_bliss)
## ----eval=TRUE, include = TRUE,fig.height=5,fig.width=7-----------------------
param$ylim <- range(range(res_bliss$beta_posterior_density[[1]]$grid_beta_t),
c(-5,5))
param$cols <- rev(heat.colors(100))
image_Bliss(res_bliss$beta_posterior_density,param,q=1)
## ----eval=TRUE, include = TRUE,fig.height=5,fig.width=7-----------------------
param$ylim <- range(range(res_bliss$beta_posterior_density[[1]]$grid_beta_t),
c(-5,5))
param$cols <- rev(heat.colors(100))
image_Bliss(res_bliss$beta_posterior_density,param,q=1)
# Bliss estimate
lines(res_bliss$data$grids[[1]],res_bliss$Bliss_estimate[[1]],type="s",lwd=2)
# Smooth estimate
lines(res_bliss$data$grids[[1]],res_bliss$Smooth_estimate[[1]],lty=2)
# True coefficient function
lines(res_bliss$data$grids[[1]],res_bliss$data$betas[[1]],col="purple",lwd=2,type="s")
## ----eval=TRUE, include = TRUE,fig.height=5,fig.width=7-----------------------
param$ylim <- range(range(res_bliss$beta_posterior_density[[1]]$grid_beta_t),
c(-5,5))
param$cols <- rev(heat.colors(100))
image_Bliss(res_bliss$beta_posterior_density,param,q=1)
# Bliss estimate
lines_bliss(res_bliss$data$grids[[1]],res_bliss$Bliss_estimate[[1]],lwd=3)
# Smooth estimate
lines(res_bliss$data$grids[[1]],res_bliss$Smooth_estimate[[1]],lty=2)
# True coefficient function
lines_bliss(res_bliss$data$grids[[1]],res_bliss$data$betas[[1]],col="purple",lwd=3)
## ----eval=TRUE, include = TRUE,fig.height=5,fig.width=7-----------------------
plot(res_bliss$alpha[[1]],type="o",xlab="time",ylab="posterior probabilities")
## ----eval=TRUE, include = TRUE,fig.height=5,fig.width=7-----------------------
plot(res_bliss$alpha[[1]],type="o",xlab="time",ylab="posterior probabilities")
abline(h=0.5,col=2,lty=2)
for(i in 1:nrow(res_bliss$support_estimate[[1]])){
segments(res_bliss$support_estimate[[1]]$begin[i],0.05,
res_bliss$support_estimate[[1]]$end[i],0.05,col="red"
)
points(res_bliss$support_estimate[[1]]$begin[i],0.05,col="red",pch="|",lwd=2)
points(res_bliss$support_estimate[[1]]$end[i],0.05,col="red",pch="|",lwd=2)
}
## ----eval=TRUE, include = TRUE,fig.height=5,fig.width=7-----------------------
res_bliss$support_estimate[[1]]
## ----eval=FALSE, include = TRUE-----------------------------------------------
# param <- list(Q=2,
# n=300,
# p=c(40,60),
# beta_shapes=c("simple","smooth"),
# grids_lim=list(c(0,1),c(0,2)))
#
# data <- sim(param)
## ----eval=FALSE, include = TRUE-----------------------------------------------
# param <- list( # define the required values of the Bliss method.
# iter=1e3, # The number of iteration of the main numerical algorithm of Bliss.
# burnin=2e2, # The number of burnin iteration for the Gibbs Sampler
# K=c(3,3)) # The number of intervals of the beta
#
# res_Bliss_mult <- fit_Bliss(data=data,param=param)
## ----eval=FALSE, include = TRUE,fig.height=5,fig.width=7----------------------
# q <- 1
# param$ylim <- range(range(res_Bliss_mult$beta_posterior_density[[q]]$grid_beta_t),
# c(-5,5))
# param$cols <- rev(heat.colors(100))
# image_Bliss(res_Bliss_mult$beta_posterior_density,param,q=q)
# lines(res_Bliss_mult$data$grids[[q]],res_Bliss_mult$Bliss_estimate[[q]],type="s",lwd=2)
# lines(res_Bliss_mult$data$grids[[q]],res_Bliss_mult$data$betas[[q]],col=2,lwd=2,type="s")
#
# ylim <- range(range(res_Bliss_mult$Bliss_estimate[[q]]),
# range(res_Bliss_mult$Smooth_estimate[[q]]))
# plot_bliss(res_Bliss_mult$data$grids[[q]],
# res_Bliss_mult$Bliss_estimate[[q]],lwd=2,ylim=ylim)
# lines(res_Bliss_mult$data$grids[[q]],
# res_Bliss_mult$Smooth_estimate[[q]],lty=2)
#
#
# q <- 2
# param$ylim <- range(range(res_Bliss_mult$beta_posterior_density[[q]]$grid_beta_t),
# c(-5,5))
# param$cols <- rev(heat.colors(100))
# image_Bliss(res_Bliss_mult$beta_posterior_density,param,q=q)
# lines(res_Bliss_mult$data$grids[[q]],res_Bliss_mult$Bliss_estimate[[q]],type="s",lwd=2)
# lines(res_Bliss_mult$data$grids[[q]],res_Bliss_mult$data$betas[[q]],col=2,lwd=2,type="l")
#
# ylim <- range(range(res_Bliss_mult$Bliss_estimate[[q]]),
# range(res_Bliss_mult$Smooth_estimate[[q]]))
# plot_bliss(res_Bliss_mult$data$grids[[q]],
# res_Bliss_mult$Bliss_estimate[[q]],lwd=2,ylim=ylim)
# lines(res_Bliss_mult$data$grids[[q]],
# res_Bliss_mult$Smooth_estimate[[q]],lty=2)
## ----session,echo=FALSE,message=FALSE, warning=FALSE--------------------------
sessionInfo()
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