The goal of bayesiansprt is to provide the results for sequential
probability ratio test under frequentist and Bayesian setup.
bayesiansprt requires the following R package: devtools (for
installation only). Please install it before installing bayesiansprt,
which can be done as follows (execute from within a fresh R session):
install.packages("devtools")
library(devtools)Once the dependencies are installed, bayesiansprt can be loaded using
the following command:
devtools::install_github("Merck/bayesiansprt")
library(bayesiansprt)This is a basic example which shows you how to solve a common problem:
library(bayesiansprt)
## basic example code
method = "Bayes"
n.sim = 10000
n.event = 60
n.start = 30
n.increment = 5
ve.null = 0.3
ve.alt = 0.75
type1 = 0.025
power = 0.9
ratio.alloc = 1
optimum.w = TRUE
b.prior = 1
seed = 134
opt.w = SPRToptimumW(n.sim = n.sim, n.event = n.event, n.start = n.start,
n.increment = 5, ve.null = ve.null, ve.alt = ve.alt,
type1 = type1, power = power, ratio.alloc = ratio.alloc,
w = seq(0.8, 0.9, by = 0.1), b.prior = b.prior, seed = seed)
#>
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#>
#> filter, lag
#> The following objects are masked from 'package:base':
#>
#> intersect, setdiff, setequal, union
results = outputSPRT(method = method, n.sim = n.sim, n.event = n.event, n.start = n.start,
n.increment = n.increment, ve.null = ve.null, ve.alt = ve.alt,
type1 = type1, power = power, ratio.alloc = ratio.alloc,
optimum.w = optimum.w, w = opt.w$weight, b.prior = b.prior, seed = seed)
results
#> $boundary
#> n_s r1 r2
#> 1 30 5 11
#> 2 35 7 13
#> 3 40 9 14
#> 4 45 10 16
#> 5 50 12 17
#> 6 55 13 19
#> 7 60 15 20
#>
#> $cumulative.prob
#> n_s r1 r2 P(RejectH0|H0) P(RejectH0|H1)
#> 1 30 5 11 0.0042 0.4246
#> 2 35 7 13 0.0084 0.6080
#> 3 40 9 14 0.0134 0.7438
#> 4 45 10 16 0.0141 0.7748
#> 5 50 12 17 0.0165 0.8425
#> 6 55 13 19 0.0166 0.8587
#> 7 60 15 20 0.0174 0.8928
#>
#> $type1.power
#> prob_dec
#> Under.H0 0.0174
#> Under.H1 0.8928
#>
#> $prob.VE
#> n_s P(x<=r1|H0) P(x>r2|H0) P(x<=r1|H1) P(x>r2|H1)
#> 1 30 0.0042 0.7576 0.4246 0.0273
#> 2 35 0.0080 0.7500 0.5976 0.0152
#> 3 40 0.0107 0.8377 0.7298 0.0217
#> 4 45 0.0058 0.8244 0.7148 0.0123
#> 5 50 0.0085 0.8867 0.8140 0.0144
#> 6 55 0.0045 0.8819 0.8019 0.0091
#> 7 60 0.0054 0.9223 0.8687 0.0116
#>
#> $percent.stop
#> n_s Avg(%)|H0 Avg(%)|H1
#> 1 30 0.7618 0.4519
#> 2 35 0.0461 0.1855
#> 3 40 0.0688 0.1428
#> 4 45 0.0217 0.0321
#> 5 50 0.0342 0.0699
#> 6 55 0.0114 0.0168
#> 7 60 0.0560 0.1010
#>
#> $summary.stat
#> Under.H0 Under.H1
#> Min. 30.0000 30.0000
#> 1st Qu. 30.0000 30.0000
#> Median 30.0000 35.0000
#> Mean 33.8930 37.6850
#> 3rd Qu. 30.0000 40.0000
#> Max. 60.0000 60.0000
#> sd 8.3049 9.8209
#>
#> $time
#> Time (in seconds)
#> Under.H0 23.71371
#> Under.H1 18.59240
#>
#> $method
#> [1] "Bayes"
#>
#> $n.event
#> [1] 60
#>
#> $n.start
#> [1] 30
#>
#> $n.increment
#> [1] 5
#>
#> $ve.null
#> [1] 0.3
#>
#> $ve.alt
#> [1] 0.75
#>
#> $type1
#> [1] 0.025
#>
#> $power
#> [1] 0.9
#>
#> $ratio.alloc
#> [1] 1
#>
#> $optimum.w
#> [1] TRUE
#>
#> $w
#> [1] 0.8
#>
#> $a.prior
#> [1] 0.5858209
#>
#> $b.prior
#> [1] 1
#>
#> $seed
#> [1] 134
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