Nothing

knitr::opts_chunk$set( collapse = TRUE, comment = "#>" )

```
library(BRDT)
```

###Generate the prior distribution of failure probability ##Beta is conjugate prior to binomial distribution #Get the non-informative prior Beta(1, 1) pi <- pi_MCSim_beta(M = 5000, seed = 10, a = 1, b = 1) #Get the consumer's risk n = 10 R <- 0.8 c <- 2 b_CR <- bconsumerrisk(n = n, c = c, pi = pi, R = R) print(b_CR) ##As n increases, CR decreases #Get the optimal test sample size thres_CR <- 0.05 #CR < 0.05 b_n <- boptimal_n(c = c, pi = pi, R = R, thres_CR = thres_CR) print(b_n)

#RDT cost Cf <- 0 Cv <- 10 n_optimal <- 24 RDTcost <- bcost_RDT(Cf = Cf, Cv = Cv, n = n_optimal) print(RDTcost) #RG Cost G <- 10000 #G can be obtained from specific reliability growth models RGcost <- bcost_RG(G = G) print(RGcost) #WS Cost Cw <- 10 N <- 1 n_optimal <- 24 WScost <- bcost_WS(Cw = 10, N = 1, n = n_optimal, c = 1, pi = pi); print(WScost[1]) #expected failure probability print(WScost[2]) #expected warranty services cost #Expected overall cost Overall_cost <- bcost_expected(Cf = Cf, Cv = Cv, n = n_optimal, G = G, Cw = Cw, N = N, c = c, pi = pi) print(Overall_cost)

#Vectors to get combinations of different R and c Rvec <- seq(0.8, 0.85, 0.01) cvec <- seq(0, 2, 1) Plan_optimal_cost <- boptimal_cost(Cf = 10, Cv = 10, G = 100, Cw = 10, N = 100, Rvec = Rvec, cvec = cvec, pi = pi, thres_CR = 0.5) print(Plan_optimal_cost)

nvec <- seq(0, 10, 1) Rvec <- seq(0.8, 0.85, 0.01) cvec <- seq(0, 2, 1) pi <- pi_MCSim_beta(M = 5000, seed = 10, a = 1, b = 1) #Get data from all combinations of n, c, R data_all <- bdata_generator(Cf = 10, Cv = 10, nvec = nvec, G = 10000, Cw = 10, N = 100, Rvec = Rvec, cvec = cvec, pi = pi, par = all(), option = c("all"), thres_CR = 0.05) head(data_all) #Get data with optimal test sample size and minimum overall costs from all combinations of c, R data_optimal <- bdata_generator(Cf = 10, Cv = 10, nvec = nvec, G = 10000, Cw = 10, N = 100, Rvec = Rvec, cvec = cvec, pi = pi, par = all(), option = c("optimal"), thres_CR = 0.05) head(data_optimal)

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