BP2D: Bayesian estimation using two dimensions Bernstein polynomial

In BayesBP: Bayesian Estimation using Bernstein Polynomial Fits Rate Matrix

Description

This function runs Metropolis-Hasting algorithm which is given setting prior and data.This algorithm starts storing coefficients when it runs halfway,so we use second halves of coefficients compute Rhat to check convergence.

Usage

BP2D(
  prior,
  ages,
  years,
  disease,
  population,
  Iterations = 2e+05,
  n_chain = 5,
  n_cluster = 1,
  nn = 2,
  interval = 100,
  RJC = 0.35,
  seed = TRUE,
  set = 1,
  double = 4
)

Arguments

prior	prior=(n0,alpha,L) where alpha is a Poisson parameter,n0 is upper bound of alpha L can be every number which is bigger than one.
ages	Range of ages.
years	Range of years.
disease	Disease matrix.
population	Population matrix.
Iterations	Iterations of chain.
n_chain	Number of Markov chain.
n_cluster	This parameter means number of cores, five cores is recommended.(default: n_cluster=1).
nn	The parameter nn is lower bound of alpha.
interval	Each hundreds save one coefficient.
RJC	Control parameter for transfer dimension.
seed	Set seed yes or not.
set	Choose seed.(defaults:set=1)
double	If R.hat >1.1 then double the iterations of times.

Value

This function will return Bayesian estimate of incidence,Stored parameters,posterior mean,posterior max and table.

Fhat	Bayesian estimate of incidence.
chain	Bayesian estimate of posterior p-value mean.
maxchain	Bayesian estimate of posterior p-value max.
store_coefficients	Two dimensional Bernstein coefficients.
output	When M-H algorithm ends,contruct the table which contains norm,mean of Fhat,maximum of Fhat,R.hat,iterations,P-value and elasped time.

References

Li-Chu Chien,Yuh-Jenn Wu,Chao A. Hsiung,Lu-Hai Wang,I-Shou Chang(2015).Smoothed Lexis Diagrams With Applications to Lung and Breast Cancer Trends in Taiwan,Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(511), pages 1000-1012, September.

See Also

Other Bayesain estimate: BP2D_coef(), BP2D_table()

Examples

# ---------------------------------------- #
library(BayesBP)
ages<-35:85
years<-1988:2007
prior<-c(10,5,2)
data(simulated_data_1)
disease<-simulated_data_1$disease
population<-simulated_data_1$population
result<-BP2D(prior,ages,years,disease,population)
# ---------------------------------------- #
# Bernstein basis
basis<-BPbasis(ages,years,10)
pdbasis1<-PD_BPbasis(ages,years,10,by = 1)
pdbasis2<-PD_BPbasis(ages,years,10,by = 2)
# Bernstein polynomial
coef<-result$store_coefficients$chain_1[[1]]
BPFhat(coef,ages,years,basis)
PD_BPFhat(coef,ages,years,pdbasis1,by = 1)
PD_BPFhat(coef,ages,years,pdbasis2,by = 2)
# Credible interval
Credible_interval(result)
PD_Credible_interval(result,by = 1)
PD_Credible_interval(result,by = 2)
# ---------------------------------------- #
# Given four prior set
ages<-35:85
years<-1988:2007
data(simulated_data_2)
disease<-simulated_data_2$disease
population<-simulated_data_2$population
p<-expand.grid(n0=c(10,20),alpha=c(5,10),LL=c(2,4))
prior_set<-p[p$n0==p$alpha*2,]
result_list<-paste0('result',1:nrow(prior_set))
for (i in seq_len(nrow(prior_set))) {
   prior<-prior_set[i,]
   assign(result_list[i],BP2D(prior,ages,years,disease,population))
   write.BP(get(result_list[i]),sprintf('%s.xlsx',result_list[i]))
}
tab<-BP2D_table(result_list)
write.BPtable(tab,'result_table.xlsx')
# ---------------------------------------- #

BayesBP documentation built on Aug. 28, 2020, 1:10 a.m.