tests/testthat.R
In sidiwang/snSMART: Small N Sequential Multiple Assignment Randomized Trial Methods
# This file is part of the standard setup for testthat.
# It is recommended that you do not modify it.
#
# Where should you do additional test configuration?
# Learn more about the roles of various files in:
# * https://r-pkgs.org/testing-design.html#sec-tests-files-overview
# * https://testthat.r-lib.org/articles/special-files.html
library(testthat)
library(snSMART)
test_that("BJSM_binary 1", {
mydata <- data_binary
BJSM_result <- BJSM_binary(
data = mydata, prior_dist = c("beta", "beta", "pareto"),
pi_prior = c(0.4, 1.6, 0.4, 1.6, 0.4, 1.6), beta_prior = c(1.6, 0.4, 3, 1),
n_MCMC_chain = 1, n.adapt = 1000, MCMC_SAMPLE = 2000, ci = 0.95,
six = TRUE, DTR = TRUE, verbose = FALSE
)
summary(BJSM_result)
print(summary(BJSM_result))
print(BJSM_result)
result = c(0.3889, 0.4371, 0.5793)
names(result) = c("pi_A", "pi_B", "pi_C")
expect_equal(BJSM_result$pi_hat_bjsm, result, tolerance = 1e-1)
})
test_that("BJSM_binary 2", {
mydata <- data_binary
BJSM_result <- BJSM_binary(
data = mydata, prior_dist = c("beta", "beta", "pareto"),
pi_prior = c(0.4, 1.6, 0.4, 1.6, 0.4, 1.6), beta_prior = c(1.6, 0.4, 3, 1),
n_MCMC_chain = 1, n.adapt = 1000, MCMC_SAMPLE = 2000, ci = 0.95,
six = TRUE, DTR = FALSE, verbose = FALSE
)
summary(BJSM_result)
print(summary(BJSM_result))
print(BJSM_result)
result = c(0.3889, 0.4371, 0.5793)
names(result) = c("pi_A", "pi_B", "pi_C")
expect_equal(BJSM_result$pi_hat_bjsm, result, tolerance = 1e-1)
})
test_that("BJSM_binary 3", {
mydata <- data_binary
BJSM_result <- BJSM_binary(
data = mydata, prior_dist = c("beta", "beta", "pareto"),
pi_prior = c(0.4, 1.6, 0.4, 1.6, 0.4, 1.6), beta_prior = c(1.6, 0.4, 3, 1),
n_MCMC_chain = 1, n.adapt = 1000, MCMC_SAMPLE = 2000, ci = 0.95,
six = TRUE, DTR = TRUE, verbose = TRUE
)
summary(BJSM_result)
print(summary(BJSM_result))
print(BJSM_result)
result = c(0.3889, 0.4371, 0.5793)
names(result) = c("pi_A", "pi_B", "pi_C")
expect_equal(BJSM_result$pi_hat_bjsm, result, tolerance = 1e-1)
})
test_that("BJSM_binary 4", {
mydata <- data_binary
BJSM_result <- BJSM_binary(
data = mydata, prior_dist = c("beta", "beta", "pareto"),
pi_prior = c(0.4, 1.6, 0.4, 1.6, 0.4, 1.6), beta_prior = c(1.6, 0.4, 3, 1),
n_MCMC_chain = 1, n.adapt = 1000, MCMC_SAMPLE = 2000, ci = 0.95,
six = TRUE, DTR = FALSE, verbose = FALSE
)
summary(BJSM_result)
print(summary(BJSM_result))
print(BJSM_result)
result = c(0.3889, 0.4371, 0.5793)
names(result) = c("pi_A", "pi_B", "pi_C")
expect_equal(BJSM_result$pi_hat_bjsm, result, tolerance = 1e-1)
})
test_that("BJSM_binary 5", {
mydata <- data_binary
BJSM_result2 <- BJSM_binary(
data = mydata, prior_dist = c("beta", "beta", "pareto"),
pi_prior = c(0.4, 1.6, 0.4, 1.6, 0.4, 1.6), beta_prior = c(1.6, 0.4, 3, 1),
n_MCMC_chain = 1, n.adapt = 10000, MCMC_SAMPLE = 60000, ci = 0.95,
six = FALSE, DTR = FALSE, verbose = FALSE
)
summary(BJSM_result2)
print(BJSM_result2)
print(summary(BJSM_result2))
result = c(0.3993, 0.4250, 0.5411)
names(result) = c("pi_A", "pi_B", "pi_C")
expect_equal(BJSM_result2$pi_hat_bjsm, result, tolerance = 1e-1)
})
test_that("BJSM_binary 6", {
mydata <- data_binary
BJSM_result2 <- BJSM_binary(
data = mydata, prior_dist = c("beta", "beta", "pareto"),
pi_prior = c(0.4, 1.6, 0.4, 1.6, 0.4, 1.6), beta_prior = c(1.6, 0.4, 3, 1),
n_MCMC_chain = 1, n.adapt = 10000, MCMC_SAMPLE = 60000, ci = 0.95,
six = FALSE, DTR = TRUE, verbose = FALSE
)
summary(BJSM_result2)
print(BJSM_result2)
print(summary(BJSM_result2))
result = c(0.3993, 0.4250, 0.5411)
names(result) = c("pi_A", "pi_B", "pi_C")
expect_equal(BJSM_result2$pi_hat_bjsm, result, tolerance = 1e-1)
})
test_that("BJSM_binary 7", {
mydata <- data_binary
BJSM_result2 <- BJSM_binary(
data = mydata, prior_dist = c("beta", "beta", "pareto"),
pi_prior = c(0.4, 1.6, 0.4, 1.6, 0.4, 1.6), beta_prior = c(1.6, 0.4, 3, 1),
n_MCMC_chain = 1, n.adapt = 10000, MCMC_SAMPLE = 60000, ci = 0.95,
six = FALSE, DTR = TRUE, verbose = TRUE
)
summary(BJSM_result2)
print(BJSM_result2)
print(summary(BJSM_result2))
result = c(0.3993, 0.4250, 0.5411)
names(result) = c("pi_A", "pi_B", "pi_C")
expect_equal(BJSM_result2$pi_hat_bjsm, result, tolerance = 1e-1)
})
test_that("BJSM_binary 7", {
mydata <- data_binary
BJSM_result2 <- BJSM_binary(
data = mydata, prior_dist = c("beta", "beta", "pareto"),
pi_prior = c(0.4, 1.6, 0.4, 1.6, 0.4, 1.6), beta_prior = c(1.6, 0.4, 3, 1),
n_MCMC_chain = 1, n.adapt = 10000, MCMC_SAMPLE = 60000, ci = 0.95,
six = FALSE, DTR = FALSE, verbose = TRUE
)
summary(BJSM_result2)
print(BJSM_result2)
print(summary(BJSM_result2))
result = c(0.3993, 0.4250, 0.5411)
names(result) = c("pi_A", "pi_B", "pi_C")
expect_equal(BJSM_result2$pi_hat_bjsm, result, tolerance = 1e-1)
})
test_that("BJSM_binary 8", {
data <- data_dose
BJSM_dose_result <- BJSM_binary(
data = data_dose, prior_dist = c("beta", "gamma"),
pi_prior = c(3, 17), normal.par = c(0.2, 100), beta_prior = c(2, 2),
n_MCMC_chain = 2, n.adapt = 1000, MCMC_SAMPLE = 6000, ci = 0.95, verbose = FALSE
)
summary(BJSM_dose_result)
print(BJSM_dose_result)
print(summary(BJSM_dose_result))
result = c(0.06971, 0.40131, 0.73859)
names(result) = c("pi_P", "pi_L", "pi_H")
expect_equal(BJSM_dose_result$pi_hat_bjsm, result, tolerance = 1e-1)
})
test_that("BJSM_binary 9", {
data <- data_dose
BJSM_dose_result <- BJSM_binary(
data = data_dose, prior_dist = c("beta", "gamma"),
pi_prior = c(3, 17), normal.par = c(0.2, 100), beta_prior = c(2, 2),
n_MCMC_chain = 2, n.adapt = 1000, MCMC_SAMPLE = 6000, ci = 0.95, verbose = TRUE
)
summary(BJSM_dose_result)
print(BJSM_dose_result)
print(summary(BJSM_dose_result))
result = c(0.06971, 0.40131, 0.73859)
names(result) = c("pi_P", "pi_L", "pi_H")
expect_equal(BJSM_dose_result$pi_hat_bjsm, result, tolerance = 1e-1)
})
test_that("BJSM_c 1", {
trialData <- trialDataMF
BJSM_result <- BJSM_c(
data = trialData, xi_prior.mean = c(50, 50, 50),
xi_prior.sd = c(50, 50, 50), phi3_prior.sd = 20, n_MCMC_chain = 1,
n.adapt = 1000, MCMC_SAMPLE = 5000, BURIN.IN = 1000, ci = 0.95, n.digits = 5, verbose = FALSE
)
summary(BJSM_result)
print(BJSM_result)
print(summary(BJSM_result))
result = 51.12
names(result) = c("xi_[1]")
expect_equal(BJSM_result$mean_estimate[c("xi_[1]")], result, tolerance = 1e-1)
})
test_that("BJSM_c 2", {
trialData <- trialDataMF
BJSM_result <- BJSM_c(
data = trialData, xi_prior.mean = c(50, 50, 50),
xi_prior.sd = c(50, 50, 50), phi3_prior.sd = 20, n_MCMC_chain = 1,
n.adapt = 1000, MCMC_SAMPLE = 5000, BURIN.IN = 1000, ci = 0.95, n.digits = 5, verbose = TRUE
)
summary(BJSM_result)
print(BJSM_result)
print(summary(BJSM_result))
result = 51.12
names(result) = c("xi_[1]")
expect_equal(BJSM_result$mean_estimate[c("xi_[1]")], result, tolerance = 1e-1)
})
test_that("group_seq 1", {
mydata <- groupseqDATA_look1
result1 <- group_seq(
data = mydata, interim = TRUE, drop_threshold_pair = c(0.5, 0.4),
prior_dist = c("beta", "beta", "pareto"), pi_prior = c(0.4, 1.6, 0.4, 1.6, 0.4, 1.6),
beta_prior = c(1.6, 0.4, 3, 1), MCMC_SAMPLE = 6000, n.adapt = 1000, n_MCMC_chain = 1
)
summary(result1)
print(result1)
print(summary(result1))
expect_equal(result1$dropped_arm, 1, tolerance = 1e-1)
})
test_that("group_seq 2", {
mydata <- groupseqDATA_look1
result1 <- group_seq(
data = mydata, interim = TRUE, drop_threshold_pair = c(0.5, 0.4),
prior_dist = c("beta", "beta", "pareto"), pi_prior = c(0.4, 1.6, 0.4, 1.6, 0.4, 1.6),
beta_prior = c(1.6, 0.4, 3, 1), MCMC_SAMPLE = 6000, n.adapt = 1000, n_MCMC_chain = 1,
DTR = TRUE, verbose = TRUE
)
summary(result1)
print(result1)
print(summary(result1))
expect_equal(result1$dropped_arm, 1, tolerance = 1e-1)
})
test_that("group_seq 3", {
mydata <- groupseqDATA_look1
result1 <- group_seq(
data = mydata, interim = TRUE, drop_threshold_pair = c(0.5, 0.4),
prior_dist = c("beta", "beta", "pareto"), pi_prior = c(0.4, 1.6, 0.4, 1.6, 0.4, 1.6),
beta_prior = c(1.6, 0.4, 3, 1), MCMC_SAMPLE = 6000, n.adapt = 1000, n_MCMC_chain = 1,
DTR = FALSE, verbose = TRUE
)
summary(result1)
print(result1)
print(summary(result1))
expect_equal(result1$dropped_arm, 1, tolerance = 1e-1)
})
test_that("group_seq 4", {
mydata <- groupseqDATA_look1
result1 <- group_seq(
data = mydata, interim = TRUE, drop_threshold_pair = c(0.5, 0.4),
prior_dist = c("beta", "beta", "pareto"), pi_prior = c(0.4, 1.6, 0.4, 1.6, 0.4, 1.6),
beta_prior = c(1.6, 0.4, 3, 1), MCMC_SAMPLE = 6000, n.adapt = 1000, n_MCMC_chain = 1,
DTR = FALSE, verbose = FALSE
)
summary(result1)
print(result1)
print(summary(result1))
expect_equal(result1$dropped_arm, 1, tolerance = 1e-1)
})
test_that("group_seq 5", {
mydata <- groupseqDATA_full
result2 <- group_seq(
data = mydata, interim = FALSE, prior_dist = c("beta", "beta", "pareto"),
pi_prior = c(0.4, 1.6, 0.4, 1.6, 0.4, 1.6),
beta_prior = c(1.6, 0.4, 3, 1), MCMC_SAMPLE = 6000, n.adapt = 1000,
n_MCMC_chain = 1, ci = 0.95, DTR = TRUE
)
summary(result2)
print(result2)
print(summary(result2))
result = c(0.3014, 0.4729, 0.6761)
names(result) = c("pi_A", "pi_B", "pi_C")
expect_equal(result2$pi_hat_bjsm, result, tolerance = 1e-1)
})
test_that("group_seq 6", {
mydata <- groupseqDATA_full
result2 <- group_seq(
data = mydata, interim = FALSE, prior_dist = c("beta", "beta", "pareto"),
pi_prior = c(0.4, 1.6, 0.4, 1.6, 0.4, 1.6),
beta_prior = c(1.6, 0.4, 3, 1), MCMC_SAMPLE = 6000, n.adapt = 1000,
n_MCMC_chain = 1, ci = 0.95, DTR = TRUE, verbose = TRUE
)
summary(result2)
print(result2)
print(summary(result2))
result = c(0.3014, 0.4729, 0.6761)
names(result) = c("pi_A", "pi_B", "pi_C")
expect_equal(result2$pi_hat_bjsm, result, tolerance = 1e-1)
})
test_that("group_seq 7", {
mydata <- groupseqDATA_full
result2 <- group_seq(
data = mydata, interim = FALSE, prior_dist = c("beta", "beta", "pareto"),
pi_prior = c(0.4, 1.6, 0.4, 1.6, 0.4, 1.6),
beta_prior = c(1.6, 0.4, 3, 1), MCMC_SAMPLE = 6000, n.adapt = 1000,
n_MCMC_chain = 1, ci = 0.95, DTR = FALSE, verbose = TRUE
)
summary(result2)
print(result2)
print(summary(result2))
result = c(0.3014, 0.4729, 0.6761)
names(result) = c("pi_A", "pi_B", "pi_C")
expect_equal(result2$pi_hat_bjsm, result, tolerance = 1e-1)
})
test_that("group_seq 8", {
mydata <- groupseqDATA_full
result2 <- group_seq(
data = mydata, interim = FALSE, prior_dist = c("beta", "beta", "pareto"),
pi_prior = c(0.4, 1.6, 0.4, 1.6, 0.4, 1.6),
beta_prior = c(1.6, 0.4, 3, 1), MCMC_SAMPLE = 6000, n.adapt = 1000,
n_MCMC_chain = 1, ci = 0.95, DTR = FALSE, verbose = FALSE
)
summary(result2)
print(result2)
print(summary(result2))
result = c(0.3014, 0.4729, 0.6761)
names(result) = c("pi_A", "pi_B", "pi_C")
expect_equal(result2$pi_hat_bjsm, result, tolerance = 1e-1)
})
test_that("group_seq 9", {
mydata <- groupseqDATA_look1
mydata$trt.1st = ifelse(mydata$trt.1st == 1, 4, mydata$trt.1st)
mydata$trt.1st = ifelse(mydata$trt.1st == 2, 1, mydata$trt.1st)
mydata$trt.1st = ifelse(mydata$trt.1st == 4, 2, mydata$trt.1st)
mydata$trt.2nd = ifelse(mydata$trt.2nd == 1, 4, mydata$trt.2nd)
mydata$trt.2nd = ifelse(mydata$trt.2nd == 2, 1, mydata$trt.2nd)
mydata$trt.2nd = ifelse(mydata$trt.2nd == 4, 2, mydata$trt.2nd)
result1 <- group_seq(
data = mydata, interim = TRUE, drop_threshold_pair = c(0.5, 0.4),
prior_dist = c("beta", "beta", "pareto"), pi_prior = c(0.4, 1.6, 0.4, 1.6, 0.4, 1.6),
beta_prior = c(1.6, 0.4, 3, 1), MCMC_SAMPLE = 6000, n.adapt = 1000, n_MCMC_chain = 1
)
summary(result1)
print(result1)
print(summary(result1))
expect_equal(result1$dropped_arm, 2, tolerance = 1e-1)
})
test_that("group_seq 10", {
mydata <- groupseqDATA_look1
mydata$trt.1st = ifelse(mydata$trt.1st == 1, 4, mydata$trt.1st)
mydata$trt.1st = ifelse(mydata$trt.1st == 3, 1, mydata$trt.1st)
mydata$trt.1st = ifelse(mydata$trt.1st == 4, 3, mydata$trt.1st)
result1 <- group_seq(
data = mydata, interim = TRUE, drop_threshold_pair = c(0.5, 0.4),
prior_dist = c("beta", "beta", "pareto"), pi_prior = c(0.4, 1.6, 0.4, 1.6, 0.4, 1.6),
beta_prior = c(1.6, 0.4, 3, 1), MCMC_SAMPLE = 6000, n.adapt = 1000, n_MCMC_chain = 1
)
summary(result1)
print(result1)
print(summary(result1))
expect_equal(result1$dropped_arm, 3, tolerance = 1e-1)
})
test_that("group_seq 11", {
mydata <- groupseqDATA_full
mydata$trt.1st = ifelse(mydata$trt.1st == 1, 4, mydata$trt.1st)
mydata$trt.1st = ifelse(mydata$trt.1st == 3, 1, mydata$trt.1st)
mydata$trt.1st = ifelse(mydata$trt.1st == 4, 3, mydata$trt.1st)
mydata$trt.2nd = ifelse(mydata$trt.2nd == 1, 4, mydata$trt.2nd)
mydata$trt.2nd = ifelse(mydata$trt.2nd == 3, 1, mydata$trt.2nd)
mydata$trt.2nd = ifelse(mydata$trt.2nd == 4, 3, mydata$trt.2nd)
result2 <- group_seq(
data = mydata, interim = FALSE, prior_dist = c("beta", "beta", "pareto"),
pi_prior = c(0.4, 1.6, 0.4, 1.6, 0.4, 1.6),
beta_prior = c(1.6, 0.4, 3, 1), MCMC_SAMPLE = 6000, n.adapt = 1000,
n_MCMC_chain = 1, ci = 0.95, DTR = FALSE, verbose = FALSE
)
summary(result2)
print(result2)
print(summary(result2))
result = c(0.6878912, 0.4730337, 0.3022561)
names(result) = c("pi_A", "pi_B", "pi_C")
expect_equal(result2$pi_hat_bjsm, result, tolerance = 1e-1)
})
test_that("group_seq 12", {
mydata <- groupseqDATA_full
mydata$trt.1st = ifelse(mydata$trt.1st == 1, 4, mydata$trt.1st)
mydata$trt.1st = ifelse(mydata$trt.1st == 2, 1, mydata$trt.1st)
mydata$trt.1st = ifelse(mydata$trt.1st == 4, 2, mydata$trt.1st)
mydata$trt.2nd = ifelse(mydata$trt.2nd == 1, 4, mydata$trt.2nd)
mydata$trt.2nd = ifelse(mydata$trt.2nd == 2, 1, mydata$trt.2nd)
mydata$trt.2nd = ifelse(mydata$trt.2nd == 4, 2, mydata$trt.2nd)
result2 <- group_seq(
data = mydata, interim = FALSE, prior_dist = c("beta", "beta", "pareto"),
pi_prior = c(0.4, 1.6, 0.4, 1.6, 0.4, 1.6),
beta_prior = c(1.6, 0.4, 3, 1), MCMC_SAMPLE = 6000, n.adapt = 1000,
n_MCMC_chain = 1, ci = 0.95, DTR = FALSE, verbose = FALSE
)
summary(result2)
print(result2)
print(summary(result2))
result = c(0.4727878, 0.3003522, 0.6828518)
names(result) = c("pi_A", "pi_B", "pi_C")
expect_equal(result2$pi_hat_bjsm, result, tolerance = 1e-1)
})
test_that("LPJSM_binary 3", {
data <- data_binary
LPJSM_result <- LPJSM_binary(data = data, six = TRUE, DTR = TRUE)
summary(LPJSM_result)
print(LPJSM_result)
print(summary(LPJSM_result))
result = c(0.2966, 0.3736, 0.4298)
names(result) = c("alphaA", "alphaB", "alphaC")
expect_equal(LPJSM_result$pi_hat, result, tolerance = 1e-1)
})
test_that("LPJSM_binary 4", {
data <- data_binary
LPJSM_result <- LPJSM_binary(data = data, six = FALSE, DTR = TRUE)
summary(LPJSM_result)
print(LPJSM_result)
print(summary(LPJSM_result))
result = c(0.2966, 0.3736, 0.4298)
names(result) = c("alphaA", "alphaB", "alphaC")
expect_equal(LPJSM_result$pi_hat, result, tolerance = 1e-1)
})
test_that("LPJSM_binary 5", {
data <- data_binary
LPJSM_result <- LPJSM_binary(data = data, six = FALSE, DTR = FALSE)
summary(LPJSM_result)
print(LPJSM_result)
print(summary(LPJSM_result))
result = c(0.2966, 0.3736, 0.4298)
names(result) = c("alphaA", "alphaB", "alphaC")
expect_equal(LPJSM_result$pi_hat, result, tolerance = 1e-1)
})
test_that("LPJSM_binary 5", {
data <- data_binary
LPJSM_result <- LPJSM_binary(data = data, six = TRUE, DTR = FALSE)
result = c(0.2966, 0.3736, 0.4298)
names(result) = c("alphaA", "alphaB", "alphaC")
summary(LPJSM_result)
print(LPJSM_result)
print(summary(LPJSM_result))
expect_equal(LPJSM_result$pi_hat, result, tolerance = 1e-1)
})
test_that("sampleSize 1", {
sampleSize <- sample_size(
pi = c(0.7, 0.5, 0.25), beta1 = 1.4, beta0 = 0.5, coverage = 0.9,
power = 0.3, mu = c(0.65, 0.55, 0.25), n = c(10, 10, 10)
)
result = 17
summary(sampleSize)
print(sampleSize)
print(summary(sampleSize))
expect_equal(sampleSize$final_N, result, tolerance = 1e-1)
})
test_that("sampleSize 2", {
sampleSize <- sample_size(
pi = c(0.7, 0.5, 0.25), beta1 = 1.4, beta0 = 0.5, coverage = 0.9,
power = 0.3, mu = c(0.65, 0.55, 0.25), n = c(10, 10, 10), verbose = TRUE
)
result = 17
summary(sampleSize)
print(sampleSize)
print(summary(sampleSize))
expect_equal(sampleSize$final_N, result, tolerance = 1e-1)
})
test_that("sampleSize 2", {
try({sampleSize <- sample_size(
pi = c(2, 2, 2), beta1 = -2, beta0 = -2, coverage = 2,
power = -2, mu = c(2, 2, 2), n = c(-2, -2, -2), verbose = TRUE
)}, silent = TRUE)
})
sidiwang/snSMART documentation built on Oct. 8, 2024, 9:31 p.m.
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# This file is part of the standard setup for testthat.
# It is recommended that you do not modify it.
#
# Where should you do additional test configuration?
# Learn more about the roles of various files in:
# * https://r-pkgs.org/testing-design.html#sec-tests-files-overview
# * https://testthat.r-lib.org/articles/special-files.html
library(testthat)
library(snSMART)
test_that("BJSM_binary 1", {
mydata <- data_binary
BJSM_result <- BJSM_binary(
data = mydata, prior_dist = c("beta", "beta", "pareto"),
pi_prior = c(0.4, 1.6, 0.4, 1.6, 0.4, 1.6), beta_prior = c(1.6, 0.4, 3, 1),
n_MCMC_chain = 1, n.adapt = 1000, MCMC_SAMPLE = 2000, ci = 0.95,
six = TRUE, DTR = TRUE, verbose = FALSE
)
summary(BJSM_result)
print(summary(BJSM_result))
print(BJSM_result)
result = c(0.3889, 0.4371, 0.5793)
names(result) = c("pi_A", "pi_B", "pi_C")
expect_equal(BJSM_result$pi_hat_bjsm, result, tolerance = 1e-1)
})
test_that("BJSM_binary 2", {
mydata <- data_binary
BJSM_result <- BJSM_binary(
data = mydata, prior_dist = c("beta", "beta", "pareto"),
pi_prior = c(0.4, 1.6, 0.4, 1.6, 0.4, 1.6), beta_prior = c(1.6, 0.4, 3, 1),
n_MCMC_chain = 1, n.adapt = 1000, MCMC_SAMPLE = 2000, ci = 0.95,
six = TRUE, DTR = FALSE, verbose = FALSE
)
summary(BJSM_result)
print(summary(BJSM_result))
print(BJSM_result)
result = c(0.3889, 0.4371, 0.5793)
names(result) = c("pi_A", "pi_B", "pi_C")
expect_equal(BJSM_result$pi_hat_bjsm, result, tolerance = 1e-1)
})
test_that("BJSM_binary 3", {
mydata <- data_binary
BJSM_result <- BJSM_binary(
data = mydata, prior_dist = c("beta", "beta", "pareto"),
pi_prior = c(0.4, 1.6, 0.4, 1.6, 0.4, 1.6), beta_prior = c(1.6, 0.4, 3, 1),
n_MCMC_chain = 1, n.adapt = 1000, MCMC_SAMPLE = 2000, ci = 0.95,
six = TRUE, DTR = TRUE, verbose = TRUE
)
summary(BJSM_result)
print(summary(BJSM_result))
print(BJSM_result)
result = c(0.3889, 0.4371, 0.5793)
names(result) = c("pi_A", "pi_B", "pi_C")
expect_equal(BJSM_result$pi_hat_bjsm, result, tolerance = 1e-1)
})
test_that("BJSM_binary 4", {
mydata <- data_binary
BJSM_result <- BJSM_binary(
data = mydata, prior_dist = c("beta", "beta", "pareto"),
pi_prior = c(0.4, 1.6, 0.4, 1.6, 0.4, 1.6), beta_prior = c(1.6, 0.4, 3, 1),
n_MCMC_chain = 1, n.adapt = 1000, MCMC_SAMPLE = 2000, ci = 0.95,
six = TRUE, DTR = FALSE, verbose = FALSE
)
summary(BJSM_result)
print(summary(BJSM_result))
print(BJSM_result)
result = c(0.3889, 0.4371, 0.5793)
names(result) = c("pi_A", "pi_B", "pi_C")
expect_equal(BJSM_result$pi_hat_bjsm, result, tolerance = 1e-1)
})
test_that("BJSM_binary 5", {
mydata <- data_binary
BJSM_result2 <- BJSM_binary(
data = mydata, prior_dist = c("beta", "beta", "pareto"),
pi_prior = c(0.4, 1.6, 0.4, 1.6, 0.4, 1.6), beta_prior = c(1.6, 0.4, 3, 1),
n_MCMC_chain = 1, n.adapt = 10000, MCMC_SAMPLE = 60000, ci = 0.95,
six = FALSE, DTR = FALSE, verbose = FALSE
)
summary(BJSM_result2)
print(BJSM_result2)
print(summary(BJSM_result2))
result = c(0.3993, 0.4250, 0.5411)
names(result) = c("pi_A", "pi_B", "pi_C")
expect_equal(BJSM_result2$pi_hat_bjsm, result, tolerance = 1e-1)
})
test_that("BJSM_binary 6", {
mydata <- data_binary
BJSM_result2 <- BJSM_binary(
data = mydata, prior_dist = c("beta", "beta", "pareto"),
pi_prior = c(0.4, 1.6, 0.4, 1.6, 0.4, 1.6), beta_prior = c(1.6, 0.4, 3, 1),
n_MCMC_chain = 1, n.adapt = 10000, MCMC_SAMPLE = 60000, ci = 0.95,
six = FALSE, DTR = TRUE, verbose = FALSE
)
summary(BJSM_result2)
print(BJSM_result2)
print(summary(BJSM_result2))
result = c(0.3993, 0.4250, 0.5411)
names(result) = c("pi_A", "pi_B", "pi_C")
expect_equal(BJSM_result2$pi_hat_bjsm, result, tolerance = 1e-1)
})
test_that("BJSM_binary 7", {
mydata <- data_binary
BJSM_result2 <- BJSM_binary(
data = mydata, prior_dist = c("beta", "beta", "pareto"),
pi_prior = c(0.4, 1.6, 0.4, 1.6, 0.4, 1.6), beta_prior = c(1.6, 0.4, 3, 1),
n_MCMC_chain = 1, n.adapt = 10000, MCMC_SAMPLE = 60000, ci = 0.95,
six = FALSE, DTR = TRUE, verbose = TRUE
)
summary(BJSM_result2)
print(BJSM_result2)
print(summary(BJSM_result2))
result = c(0.3993, 0.4250, 0.5411)
names(result) = c("pi_A", "pi_B", "pi_C")
expect_equal(BJSM_result2$pi_hat_bjsm, result, tolerance = 1e-1)
})
test_that("BJSM_binary 7", {
mydata <- data_binary
BJSM_result2 <- BJSM_binary(
data = mydata, prior_dist = c("beta", "beta", "pareto"),
pi_prior = c(0.4, 1.6, 0.4, 1.6, 0.4, 1.6), beta_prior = c(1.6, 0.4, 3, 1),
n_MCMC_chain = 1, n.adapt = 10000, MCMC_SAMPLE = 60000, ci = 0.95,
six = FALSE, DTR = FALSE, verbose = TRUE
)
summary(BJSM_result2)
print(BJSM_result2)
print(summary(BJSM_result2))
result = c(0.3993, 0.4250, 0.5411)
names(result) = c("pi_A", "pi_B", "pi_C")
expect_equal(BJSM_result2$pi_hat_bjsm, result, tolerance = 1e-1)
})
test_that("BJSM_binary 8", {
data <- data_dose
BJSM_dose_result <- BJSM_binary(
data = data_dose, prior_dist = c("beta", "gamma"),
pi_prior = c(3, 17), normal.par = c(0.2, 100), beta_prior = c(2, 2),
n_MCMC_chain = 2, n.adapt = 1000, MCMC_SAMPLE = 6000, ci = 0.95, verbose = FALSE
)
summary(BJSM_dose_result)
print(BJSM_dose_result)
print(summary(BJSM_dose_result))
result = c(0.06971, 0.40131, 0.73859)
names(result) = c("pi_P", "pi_L", "pi_H")
expect_equal(BJSM_dose_result$pi_hat_bjsm, result, tolerance = 1e-1)
})
test_that("BJSM_binary 9", {
data <- data_dose
BJSM_dose_result <- BJSM_binary(
data = data_dose, prior_dist = c("beta", "gamma"),
pi_prior = c(3, 17), normal.par = c(0.2, 100), beta_prior = c(2, 2),
n_MCMC_chain = 2, n.adapt = 1000, MCMC_SAMPLE = 6000, ci = 0.95, verbose = TRUE
)
summary(BJSM_dose_result)
print(BJSM_dose_result)
print(summary(BJSM_dose_result))
result = c(0.06971, 0.40131, 0.73859)
names(result) = c("pi_P", "pi_L", "pi_H")
expect_equal(BJSM_dose_result$pi_hat_bjsm, result, tolerance = 1e-1)
})
test_that("BJSM_c 1", {
trialData <- trialDataMF
BJSM_result <- BJSM_c(
data = trialData, xi_prior.mean = c(50, 50, 50),
xi_prior.sd = c(50, 50, 50), phi3_prior.sd = 20, n_MCMC_chain = 1,
n.adapt = 1000, MCMC_SAMPLE = 5000, BURIN.IN = 1000, ci = 0.95, n.digits = 5, verbose = FALSE
)
summary(BJSM_result)
print(BJSM_result)
print(summary(BJSM_result))
result = 51.12
names(result) = c("xi_[1]")
expect_equal(BJSM_result$mean_estimate[c("xi_[1]")], result, tolerance = 1e-1)
})
test_that("BJSM_c 2", {
trialData <- trialDataMF
BJSM_result <- BJSM_c(
data = trialData, xi_prior.mean = c(50, 50, 50),
xi_prior.sd = c(50, 50, 50), phi3_prior.sd = 20, n_MCMC_chain = 1,
n.adapt = 1000, MCMC_SAMPLE = 5000, BURIN.IN = 1000, ci = 0.95, n.digits = 5, verbose = TRUE
)
summary(BJSM_result)
print(BJSM_result)
print(summary(BJSM_result))
result = 51.12
names(result) = c("xi_[1]")
expect_equal(BJSM_result$mean_estimate[c("xi_[1]")], result, tolerance = 1e-1)
})
test_that("group_seq 1", {
mydata <- groupseqDATA_look1
result1 <- group_seq(
data = mydata, interim = TRUE, drop_threshold_pair = c(0.5, 0.4),
prior_dist = c("beta", "beta", "pareto"), pi_prior = c(0.4, 1.6, 0.4, 1.6, 0.4, 1.6),
beta_prior = c(1.6, 0.4, 3, 1), MCMC_SAMPLE = 6000, n.adapt = 1000, n_MCMC_chain = 1
)
summary(result1)
print(result1)
print(summary(result1))
expect_equal(result1$dropped_arm, 1, tolerance = 1e-1)
})
test_that("group_seq 2", {
mydata <- groupseqDATA_look1
result1 <- group_seq(
data = mydata, interim = TRUE, drop_threshold_pair = c(0.5, 0.4),
prior_dist = c("beta", "beta", "pareto"), pi_prior = c(0.4, 1.6, 0.4, 1.6, 0.4, 1.6),
beta_prior = c(1.6, 0.4, 3, 1), MCMC_SAMPLE = 6000, n.adapt = 1000, n_MCMC_chain = 1,
DTR = TRUE, verbose = TRUE
)
summary(result1)
print(result1)
print(summary(result1))
expect_equal(result1$dropped_arm, 1, tolerance = 1e-1)
})
test_that("group_seq 3", {
mydata <- groupseqDATA_look1
result1 <- group_seq(
data = mydata, interim = TRUE, drop_threshold_pair = c(0.5, 0.4),
prior_dist = c("beta", "beta", "pareto"), pi_prior = c(0.4, 1.6, 0.4, 1.6, 0.4, 1.6),
beta_prior = c(1.6, 0.4, 3, 1), MCMC_SAMPLE = 6000, n.adapt = 1000, n_MCMC_chain = 1,
DTR = FALSE, verbose = TRUE
)
summary(result1)
print(result1)
print(summary(result1))
expect_equal(result1$dropped_arm, 1, tolerance = 1e-1)
})
test_that("group_seq 4", {
mydata <- groupseqDATA_look1
result1 <- group_seq(
data = mydata, interim = TRUE, drop_threshold_pair = c(0.5, 0.4),
prior_dist = c("beta", "beta", "pareto"), pi_prior = c(0.4, 1.6, 0.4, 1.6, 0.4, 1.6),
beta_prior = c(1.6, 0.4, 3, 1), MCMC_SAMPLE = 6000, n.adapt = 1000, n_MCMC_chain = 1,
DTR = FALSE, verbose = FALSE
)
summary(result1)
print(result1)
print(summary(result1))
expect_equal(result1$dropped_arm, 1, tolerance = 1e-1)
})
test_that("group_seq 5", {
mydata <- groupseqDATA_full
result2 <- group_seq(
data = mydata, interim = FALSE, prior_dist = c("beta", "beta", "pareto"),
pi_prior = c(0.4, 1.6, 0.4, 1.6, 0.4, 1.6),
beta_prior = c(1.6, 0.4, 3, 1), MCMC_SAMPLE = 6000, n.adapt = 1000,
n_MCMC_chain = 1, ci = 0.95, DTR = TRUE
)
summary(result2)
print(result2)
print(summary(result2))
result = c(0.3014, 0.4729, 0.6761)
names(result) = c("pi_A", "pi_B", "pi_C")
expect_equal(result2$pi_hat_bjsm, result, tolerance = 1e-1)
})
test_that("group_seq 6", {
mydata <- groupseqDATA_full
result2 <- group_seq(
data = mydata, interim = FALSE, prior_dist = c("beta", "beta", "pareto"),
pi_prior = c(0.4, 1.6, 0.4, 1.6, 0.4, 1.6),
beta_prior = c(1.6, 0.4, 3, 1), MCMC_SAMPLE = 6000, n.adapt = 1000,
n_MCMC_chain = 1, ci = 0.95, DTR = TRUE, verbose = TRUE
)
summary(result2)
print(result2)
print(summary(result2))
result = c(0.3014, 0.4729, 0.6761)
names(result) = c("pi_A", "pi_B", "pi_C")
expect_equal(result2$pi_hat_bjsm, result, tolerance = 1e-1)
})
test_that("group_seq 7", {
mydata <- groupseqDATA_full
result2 <- group_seq(
data = mydata, interim = FALSE, prior_dist = c("beta", "beta", "pareto"),
pi_prior = c(0.4, 1.6, 0.4, 1.6, 0.4, 1.6),
beta_prior = c(1.6, 0.4, 3, 1), MCMC_SAMPLE = 6000, n.adapt = 1000,
n_MCMC_chain = 1, ci = 0.95, DTR = FALSE, verbose = TRUE
)
summary(result2)
print(result2)
print(summary(result2))
result = c(0.3014, 0.4729, 0.6761)
names(result) = c("pi_A", "pi_B", "pi_C")
expect_equal(result2$pi_hat_bjsm, result, tolerance = 1e-1)
})
test_that("group_seq 8", {
mydata <- groupseqDATA_full
result2 <- group_seq(
data = mydata, interim = FALSE, prior_dist = c("beta", "beta", "pareto"),
pi_prior = c(0.4, 1.6, 0.4, 1.6, 0.4, 1.6),
beta_prior = c(1.6, 0.4, 3, 1), MCMC_SAMPLE = 6000, n.adapt = 1000,
n_MCMC_chain = 1, ci = 0.95, DTR = FALSE, verbose = FALSE
)
summary(result2)
print(result2)
print(summary(result2))
result = c(0.3014, 0.4729, 0.6761)
names(result) = c("pi_A", "pi_B", "pi_C")
expect_equal(result2$pi_hat_bjsm, result, tolerance = 1e-1)
})
test_that("group_seq 9", {
mydata <- groupseqDATA_look1
mydata$trt.1st = ifelse(mydata$trt.1st == 1, 4, mydata$trt.1st)
mydata$trt.1st = ifelse(mydata$trt.1st == 2, 1, mydata$trt.1st)
mydata$trt.1st = ifelse(mydata$trt.1st == 4, 2, mydata$trt.1st)
mydata$trt.2nd = ifelse(mydata$trt.2nd == 1, 4, mydata$trt.2nd)
mydata$trt.2nd = ifelse(mydata$trt.2nd == 2, 1, mydata$trt.2nd)
mydata$trt.2nd = ifelse(mydata$trt.2nd == 4, 2, mydata$trt.2nd)
result1 <- group_seq(
data = mydata, interim = TRUE, drop_threshold_pair = c(0.5, 0.4),
prior_dist = c("beta", "beta", "pareto"), pi_prior = c(0.4, 1.6, 0.4, 1.6, 0.4, 1.6),
beta_prior = c(1.6, 0.4, 3, 1), MCMC_SAMPLE = 6000, n.adapt = 1000, n_MCMC_chain = 1
)
summary(result1)
print(result1)
print(summary(result1))
expect_equal(result1$dropped_arm, 2, tolerance = 1e-1)
})
test_that("group_seq 10", {
mydata <- groupseqDATA_look1
mydata$trt.1st = ifelse(mydata$trt.1st == 1, 4, mydata$trt.1st)
mydata$trt.1st = ifelse(mydata$trt.1st == 3, 1, mydata$trt.1st)
mydata$trt.1st = ifelse(mydata$trt.1st == 4, 3, mydata$trt.1st)
result1 <- group_seq(
data = mydata, interim = TRUE, drop_threshold_pair = c(0.5, 0.4),
prior_dist = c("beta", "beta", "pareto"), pi_prior = c(0.4, 1.6, 0.4, 1.6, 0.4, 1.6),
beta_prior = c(1.6, 0.4, 3, 1), MCMC_SAMPLE = 6000, n.adapt = 1000, n_MCMC_chain = 1
)
summary(result1)
print(result1)
print(summary(result1))
expect_equal(result1$dropped_arm, 3, tolerance = 1e-1)
})
test_that("group_seq 11", {
mydata <- groupseqDATA_full
mydata$trt.1st = ifelse(mydata$trt.1st == 1, 4, mydata$trt.1st)
mydata$trt.1st = ifelse(mydata$trt.1st == 3, 1, mydata$trt.1st)
mydata$trt.1st = ifelse(mydata$trt.1st == 4, 3, mydata$trt.1st)
mydata$trt.2nd = ifelse(mydata$trt.2nd == 1, 4, mydata$trt.2nd)
mydata$trt.2nd = ifelse(mydata$trt.2nd == 3, 1, mydata$trt.2nd)
mydata$trt.2nd = ifelse(mydata$trt.2nd == 4, 3, mydata$trt.2nd)
result2 <- group_seq(
data = mydata, interim = FALSE, prior_dist = c("beta", "beta", "pareto"),
pi_prior = c(0.4, 1.6, 0.4, 1.6, 0.4, 1.6),
beta_prior = c(1.6, 0.4, 3, 1), MCMC_SAMPLE = 6000, n.adapt = 1000,
n_MCMC_chain = 1, ci = 0.95, DTR = FALSE, verbose = FALSE
)
summary(result2)
print(result2)
print(summary(result2))
result = c(0.6878912, 0.4730337, 0.3022561)
names(result) = c("pi_A", "pi_B", "pi_C")
expect_equal(result2$pi_hat_bjsm, result, tolerance = 1e-1)
})
test_that("group_seq 12", {
mydata <- groupseqDATA_full
mydata$trt.1st = ifelse(mydata$trt.1st == 1, 4, mydata$trt.1st)
mydata$trt.1st = ifelse(mydata$trt.1st == 2, 1, mydata$trt.1st)
mydata$trt.1st = ifelse(mydata$trt.1st == 4, 2, mydata$trt.1st)
mydata$trt.2nd = ifelse(mydata$trt.2nd == 1, 4, mydata$trt.2nd)
mydata$trt.2nd = ifelse(mydata$trt.2nd == 2, 1, mydata$trt.2nd)
mydata$trt.2nd = ifelse(mydata$trt.2nd == 4, 2, mydata$trt.2nd)
result2 <- group_seq(
data = mydata, interim = FALSE, prior_dist = c("beta", "beta", "pareto"),
pi_prior = c(0.4, 1.6, 0.4, 1.6, 0.4, 1.6),
beta_prior = c(1.6, 0.4, 3, 1), MCMC_SAMPLE = 6000, n.adapt = 1000,
n_MCMC_chain = 1, ci = 0.95, DTR = FALSE, verbose = FALSE
)
summary(result2)
print(result2)
print(summary(result2))
result = c(0.4727878, 0.3003522, 0.6828518)
names(result) = c("pi_A", "pi_B", "pi_C")
expect_equal(result2$pi_hat_bjsm, result, tolerance = 1e-1)
})
test_that("LPJSM_binary 3", {
data <- data_binary
LPJSM_result <- LPJSM_binary(data = data, six = TRUE, DTR = TRUE)
summary(LPJSM_result)
print(LPJSM_result)
print(summary(LPJSM_result))
result = c(0.2966, 0.3736, 0.4298)
names(result) = c("alphaA", "alphaB", "alphaC")
expect_equal(LPJSM_result$pi_hat, result, tolerance = 1e-1)
})
test_that("LPJSM_binary 4", {
data <- data_binary
LPJSM_result <- LPJSM_binary(data = data, six = FALSE, DTR = TRUE)
summary(LPJSM_result)
print(LPJSM_result)
print(summary(LPJSM_result))
result = c(0.2966, 0.3736, 0.4298)
names(result) = c("alphaA", "alphaB", "alphaC")
expect_equal(LPJSM_result$pi_hat, result, tolerance = 1e-1)
})
test_that("LPJSM_binary 5", {
data <- data_binary
LPJSM_result <- LPJSM_binary(data = data, six = FALSE, DTR = FALSE)
summary(LPJSM_result)
print(LPJSM_result)
print(summary(LPJSM_result))
result = c(0.2966, 0.3736, 0.4298)
names(result) = c("alphaA", "alphaB", "alphaC")
expect_equal(LPJSM_result$pi_hat, result, tolerance = 1e-1)
})
test_that("LPJSM_binary 5", {
data <- data_binary
LPJSM_result <- LPJSM_binary(data = data, six = TRUE, DTR = FALSE)
result = c(0.2966, 0.3736, 0.4298)
names(result) = c("alphaA", "alphaB", "alphaC")
summary(LPJSM_result)
print(LPJSM_result)
print(summary(LPJSM_result))
expect_equal(LPJSM_result$pi_hat, result, tolerance = 1e-1)
})
test_that("sampleSize 1", {
sampleSize <- sample_size(
pi = c(0.7, 0.5, 0.25), beta1 = 1.4, beta0 = 0.5, coverage = 0.9,
power = 0.3, mu = c(0.65, 0.55, 0.25), n = c(10, 10, 10)
)
result = 17
summary(sampleSize)
print(sampleSize)
print(summary(sampleSize))
expect_equal(sampleSize$final_N, result, tolerance = 1e-1)
})
test_that("sampleSize 2", {
sampleSize <- sample_size(
pi = c(0.7, 0.5, 0.25), beta1 = 1.4, beta0 = 0.5, coverage = 0.9,
power = 0.3, mu = c(0.65, 0.55, 0.25), n = c(10, 10, 10), verbose = TRUE
)
result = 17
summary(sampleSize)
print(sampleSize)
print(summary(sampleSize))
expect_equal(sampleSize$final_N, result, tolerance = 1e-1)
})
test_that("sampleSize 2", {
try({sampleSize <- sample_size(
pi = c(2, 2, 2), beta1 = -2, beta0 = -2, coverage = 2,
power = -2, mu = c(2, 2, 2), n = c(-2, -2, -2), verbose = TRUE
)}, silent = TRUE)
})
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