Nothing
## ----echo=F--------------------------------------------------------------
### get knitr just the way we like it
knitr::opts_chunk$set(
message = FALSE,
warning = FALSE,
error = FALSE,
tidy = FALSE,
cache = FALSE
)
## ------------------------------------------------------------------------
library(ASSISTant)
## Various settings
settings <- list(setting1 = list(N = c(250, 400, 550), type1Error = 0.025,
eps = 1/2, type2Error = 0.1),
setting2 = list(N = c(250, 400, 550), type1Error = 0.05,
eps = 1/2, type2Error = 0.1),
setting3 = list(N = c(250, 400, 550), type1Error = 0.1,
eps = 1/2, type2Error = 0.2),
setting4 = list(N = c(250, 400, 550), type1Error = 0.2,
eps = 1/2, type2Error = 0.3))
## ------------------------------------------------------------------------
scenarios <- list(
scenario0 = list(prevalence = rep(1/6, 6), mean = matrix(0, 2, 6),
sd = matrix(1, 2, 6)),
scenario1 = list(prevalence = rep(1/6, 6), mean = rbind(rep(0, 6),
c(0.5, 0.4, 0.3, 0, 0, 0)),
sd = matrix(1, 2, 6)),
scenario2 = list(prevalence = rep(1/6, 6), mean = rbind(rep(0, 6),
c(0.3, 0.3, 0, 0, 0, 0)),
sd = matrix(1, 2, 6)),
scenario3 = list(prevalence = rep(1/6, 6), mean = rbind(rep(0, 6), rep(0.3, 6)),
sd = matrix(1, 2, 6)),
scenario4 = list(prevalence = rep(1/6, 6), mean = rbind(rep(0, 6),
c(0.4, 0.3, 0.2, 0, 0, 0)),
sd = matrix(1, 2, 6)),
scenario5 = list(prevalence = rep(1/6, 6), mean = rbind(rep(0, 6),
c(0.5, 0.5, 0.3, 0.3, 0.1, 0.1)),
sd = matrix(1, 2, 6)),
scenario6 = list(prevalence = rep(1/6, 6), mean = rbind(rep(0, 6),
c(0.6, 0.6, -0.3, -0.3, -0.3, -0.3)),
sd = matrix(1, 2, 6)),
scenario7 = list(prevalence = rep(1/6, 6), mean = rbind(rep(0, 6), rep(0.01, 6)),
sd = matrix(1, 2, 6)), ## very small effect
scenario8 = list(prevalence = rep(1/6, 6), mean = rbind(rep(0, 6), rep(0.3, 6)),
sd = matrix(1, 2, 6)), ## moderate negative effect
scenario9 = list(prevalence = rep(1/6, 6), mean = rbind(rep(0, 6),
c(0.9, 0.3, 0, -0.1, -0.4, -0.7)),
sd = matrix(1, 2, 6)), ## single strong effect with negatives thrown in
scenario10 = list(prevalence = rep(1/6, 6), mean = rbind(rep(0, 6), rep(-0.01, 6)),
sd = matrix(1, 2, 6)) ## very small negative effect
)
## ------------------------------------------------------------------------
rngSeed <- 2128783
set.seed(rngSeed)
for (setting in names(settings)) {
trialParameters <- settings[[setting]]
for (scenario in names(scenarios)) {
designParameters <- scenarios[[scenario]]
cat("##############################\n")
print(sprintf("%s/%s", setting, scenario))
cat("##############################\n")
designA <- ASSISTDesign$new(trialParameters = trialParameters,
designParameters = designParameters)
print(designA)
result <- designA$explore(numberOfSimulations = 5000,
rngSeed = rngSeed,
showProgress = FALSE)
analysis <- designA$analyze(result)
print(designA$summary(analysis))
rngSeed <- floor(runif(100000 * runif(1)))
}
}
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