BP2D {BayesBP} | R Documentation |
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.
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
)
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. |
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. |
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.
Other Bayesain estimate:
BP2D_coef()
,
BP2D_table()
# ---------------------------------------- #
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')
# ---------------------------------------- #