Nothing
test_that("MCMC: independent binary logit", {
n_chains <- 2
link <- "logit"
data <- dreamer_data_independent_binary(
n_cohorts = c(10, 20, 30),
dose = c(1, 3, 5),
b1 = 1:3,
link = link
)
mcmc <- dreamer_mcmc(
data,
mod = model_independent_binary(
mu_b1 = 0,
sigma_b1 = 1,
link = link
),
n_iter = 5,
silent = TRUE,
convergence_warn = FALSE,
n_chains = n_chains
)
doses <- attr(mcmc, "doses")
assert_mcmc_format(mcmc, n_chains)
# dreamer post
test_posterior(
mcmc,
doses = doses,
prob = c(.25, .75),
`b1[1]` = 1:10 / 100,
`b1[2]` = 2:11 / 100,
`b1[3]` = 3:12 / 100,
true_responses = rlang::expr(
ilogit(
matrix(c(`b1[1]`, `b1[2]`, `b1[3]`), ncol = 3)[, which(dose == !!doses)]
)
)
)
# with dose adjustment
test_posterior(
mcmc,
doses = c(1, 3, 5),
reference_dose = 3,
prob = c(.25, .75),
`b1[1]` = 1:10 / 100,
`b1[2]` = 2:11 / 100,
`b1[3]` = 3:12 / 100,
true_responses = rlang::expr(
ilogit(
matrix(c(`b1[1]`, `b1[2]`, `b1[3]`), ncol = 3)[, which(dose == !!doses)]
) -
ilogit(
matrix(
c(`b1[1]`, `b1[2]`, `b1[3]`),
ncol = 3
)[, which(reference_dose == !!doses)]
)
)
)
})
test_that("MCMC: independent binary probit", {
n_chains <- 2
link <- "probit"
data <- dreamer_data_independent_binary(
n_cohorts = c(10, 20, 30),
dose = c(1, 3, 5),
b1 = 1:3,
link = link
)
mcmc <- dreamer_mcmc(
data,
mod = model_independent_binary(
mu_b1 = 0,
sigma_b1 = 1,
link = link
),
n_iter = 5,
silent = TRUE,
convergence_warn = FALSE,
n_chains = n_chains
)
doses <- attr(mcmc, "doses")
assert_mcmc_format(mcmc, n_chains)
# dreamer post
test_posterior(
mcmc,
doses = doses,
prob = c(.25, .75),
`b1[1]` = 1:10 / 100,
`b1[2]` = 2:11 / 100,
`b1[3]` = 3:12 / 100,
true_responses = rlang::expr(
iprobit(
matrix(c(`b1[1]`, `b1[2]`, `b1[3]`), ncol = 3)[, which(dose == !!doses)]
)
)
)
# with dose adjustment
test_posterior(
mcmc,
doses = c(1, 3, 5),
reference_dose = 3,
prob = c(.25, .75),
`b1[1]` = 1:10 / 100,
`b1[2]` = 2:11 / 100,
`b1[3]` = 3:12 / 100,
true_responses = rlang::expr(
iprobit(
matrix(
c(`b1[1]`, `b1[2]`, `b1[3]`),
ncol = 3
)[, which(dose == !!doses)]
) -
iprobit(
matrix(
c(`b1[1]`, `b1[2]`, `b1[3]`),
ncol = 3
)[, which(reference_dose == !!doses)]
)
)
)
})
test_that("MCMC: independent binary logit long linear", {
n_chains <- 2
t_max <- 4
times <- c(0, 2, 4)
link <- "logit"
data <- dreamer_data_independent_binary(
n_cohorts = c(10, 20, 30),
dose = c(1, 3, 5),
b1 = 1:3,
link = link,
longitudinal = "linear",
a = .5,
times = times,
t_max = t_max
)
mcmc <- dreamer_mcmc(
data,
mod = model_independent_binary(
mu_b1 = 0,
sigma_b1 = 1,
link = link,
longitudinal = model_longitudinal_linear(0, 1, t_max)
),
n_iter = 5,
silent = TRUE,
convergence_warn = FALSE,
n_chains = n_chains
)
doses <- attr(mcmc, "doses")
assert_mcmc_format(mcmc, n_chains, times)
test_posterior(
mcmc,
doses = doses,
times = c(1, 5, 2),
prob = c(.25, .75),
a = 10:1 / 100,
`b1[1]` = 1:10 / 100,
`b1[2]` = 2:11 / 100,
`b1[3]` = 3:12 / 100,
true_responses = rlang::expr(
ilogit(
a + (time / !!t_max) *
matrix(
c(`b1[1]`, `b1[2]`, `b1[3]`),
ncol = 3
)[, which(dose == !!doses)]
)
)
)
test_posterior(
mcmc,
doses = doses,
times = c(1, 5, 2),
reference_dose = 3,
prob = c(.25, .75),
a = 10:1 / 100,
`b1[1]` = 1:10 / 100,
`b1[2]` = 2:11 / 100,
`b1[3]` = 3:12 / 100,
true_responses = rlang::expr(
ilogit(
a + (time / !!t_max) *
matrix(
c(`b1[1]`, `b1[2]`, `b1[3]`),
ncol = 3
)[, which(dose == !!doses)]
) -
ilogit(
a + (time / !!t_max) *
matrix(
c(`b1[1]`, `b1[2]`, `b1[3]`),
ncol = 3
)[, which(reference_dose == !!doses)]
)
)
)
})
test_that("MCMC: independent binary probit long linear", {
n_chains <- 2
t_max <- 4
times <- c(0, 2, 4)
link <- "probit"
data <- dreamer_data_independent_binary(
n_cohorts = c(10, 20, 30),
dose = c(1, 3, 5),
b1 = 1:3,
link = link,
longitudinal = "linear",
a = .5,
times = times,
t_max = t_max
)
mcmc <- dreamer_mcmc(
data,
mod = model_independent_binary(
mu_b1 = 0,
sigma_b1 = 1,
link = link,
longitudinal = model_longitudinal_linear(0, 1, t_max)
),
n_iter = 5,
silent = TRUE,
convergence_warn = FALSE,
n_chains = n_chains
)
doses <- attr(mcmc, "doses")
assert_mcmc_format(mcmc, n_chains, times)
test_posterior(
mcmc,
doses = doses,
times = c(1, 5, 2),
prob = c(.25, .75),
a = 10:1 / 100,
`b1[1]` = 1:10 / 100,
`b1[2]` = 2:11 / 100,
`b1[3]` = 3:12 / 100,
true_responses = rlang::expr(
iprobit(
a + (time / !!t_max) *
matrix(
c(`b1[1]`, `b1[2]`, `b1[3]`),
ncol = 3
)[, which(dose == !!doses)]
)
)
)
test_posterior(
mcmc,
doses = doses,
times = c(1, 5, 2),
reference_dose = 3,
prob = c(.25, .75),
a = 10:1 / 100,
`b1[1]` = 1:10 / 100,
`b1[2]` = 2:11 / 100,
`b1[3]` = 3:12 / 100,
true_responses = rlang::expr(
iprobit(
a + (time / !!t_max) *
matrix(
c(`b1[1]`, `b1[2]`, `b1[3]`),
ncol = 3
)[, which(dose == !!doses)]
) -
iprobit(
a + (time / !!t_max) *
matrix(
c(`b1[1]`, `b1[2]`, `b1[3]`),
ncol = 3
)[, which(reference_dose == !!doses)]
)
)
)
})
test_that("MCMC: independent binary logit long ITP", {
n_chains <- 2
t_max <- 4
times <- c(0, 2, 4)
link <- "logit"
data <- dreamer_data_independent_binary(
n_cohorts = c(10, 20, 30),
dose = c(1, 3, 5),
b1 = 1:3,
link = link,
longitudinal = "linear",
a = .5,
times = times,
t_max = t_max
)
mcmc <- dreamer_mcmc(
data,
mod = model_independent_binary(
mu_b1 = 0,
sigma_b1 = 1,
link = link,
longitudinal = model_longitudinal_itp(0, 1, t_max = t_max)
),
n_iter = 5,
silent = TRUE,
convergence_warn = FALSE,
n_chains = n_chains
)
doses <- attr(mcmc, "doses")
assert_mcmc_format(mcmc, n_chains, times)
test_posterior(
mcmc,
doses = doses,
times = c(1, 5, 2),
prob = c(.25, .75),
a = 10:1 / 100,
c1 = seq(.1, 3, length = 10) / 100,
`b1[1]` = 1:10 / 100,
`b1[2]` = 2:11 / 100,
`b1[3]` = 3:12 / 100,
true_responses = rlang::expr(
ilogit(
a + (1 - exp(- c1 * time)) / (1 - exp(- c1 * !!t_max)) *
matrix(
c(`b1[1]`, `b1[2]`, `b1[3]`),
ncol = 3
)[, which(dose == !!doses)]
)
)
)
test_posterior(
mcmc,
doses = doses,
times = c(1, 5, 2),
reference_dose = 3,
prob = c(.25, .75),
a = 10:1 / 100,
c1 = seq(.1, 3, length = 10) / 100,
`b1[1]` = 1:10 / 100,
`b1[2]` = 2:11 / 100,
`b1[3]` = 3:12 / 100,
true_responses = rlang::expr(
ilogit(
a + (1 - exp(- c1 * time)) / (1 - exp(- c1 * !!t_max)) *
matrix(
c(`b1[1]`, `b1[2]`, `b1[3]`),
ncol = 3
)[, which(dose == !!doses)]
) -
ilogit(
a + (1 - exp(- c1 * time)) / (1 - exp(- c1 * !!t_max)) *
matrix(
c(`b1[1]`, `b1[2]`, `b1[3]`),
ncol = 3
)[, which(reference_dose == !!doses)]
)
)
)
})
test_that("MCMC: independent binary probit long ITP", {
n_chains <- 2
t_max <- 4
times <- c(0, 2, 4)
link <- "probit"
data <- dreamer_data_independent_binary(
n_cohorts = c(10, 20, 30),
dose = c(1, 3, 5),
b1 = 1:3,
link = link,
longitudinal = "linear",
a = .5,
times = times,
t_max = t_max
)
mcmc <- dreamer_mcmc(
data,
mod = model_independent_binary(
mu_b1 = 0,
sigma_b1 = 1,
link = link,
longitudinal = model_longitudinal_itp(0, 1, t_max = t_max)
),
n_iter = 5,
silent = TRUE,
convergence_warn = FALSE,
n_chains = n_chains
)
doses <- attr(mcmc, "doses")
assert_mcmc_format(mcmc, n_chains, times)
test_posterior(
mcmc,
doses = doses,
times = c(1, 5, 2),
prob = c(.25, .75),
a = 10:1 / 100,
c1 = seq(.1, 3, length = 10) / 100,
`b1[1]` = 1:10 / 100,
`b1[2]` = 2:11 / 100,
`b1[3]` = 3:12 / 100,
true_responses = rlang::expr(
iprobit(
a + (1 - exp(- c1 * time)) / (1 - exp(- c1 * !!t_max)) *
matrix(
c(`b1[1]`, `b1[2]`, `b1[3]`),
ncol = 3
)[, which(dose == !!doses)]
)
)
)
test_posterior(
mcmc,
doses = doses,
times = c(1, 5, 2),
reference_dose = 3,
prob = c(.25, .75),
a = 10:1 / 100,
c1 = seq(.1, 3, length = 10) / 100,
`b1[1]` = 1:10 / 100,
`b1[2]` = 2:11 / 100,
`b1[3]` = 3:12 / 100,
true_responses = rlang::expr(
iprobit(
a + (1 - exp(- c1 * time)) / (1 - exp(- c1 * !!t_max)) *
matrix(
c(`b1[1]`, `b1[2]`, `b1[3]`),
ncol = 3
)[, which(dose == !!doses)]
) -
iprobit(
a + (1 - exp(- c1 * time)) / (1 - exp(- c1 * !!t_max)) *
matrix(
c(`b1[1]`, `b1[2]`, `b1[3]`),
ncol = 3
)[, which(reference_dose == !!doses)]
)
)
)
})
test_that("MCMC: independent binary logit long IDP", {
n_chains <- 2
t_max <- 4
times <- c(0, 2, 4)
link <- "logit"
data <- dreamer_data_independent_binary(
n_cohorts = c(10, 20, 30),
dose = c(1, 3, 5),
b1 = 1:3,
link = link,
longitudinal = "linear",
a = .5,
times = times,
t_max = t_max
)
mcmc <- dreamer_mcmc(
data,
mod = model_independent_binary(
mu_b1 = 0,
sigma_b1 = 1,
link = link,
longitudinal = model_longitudinal_idp(0, 1, t_max = t_max)
),
n_iter = 5,
silent = TRUE,
convergence_warn = FALSE,
n_chains = n_chains
)
doses <- attr(mcmc, "doses")
assert_mcmc_format(mcmc, n_chains, times)
test_posterior(
mcmc,
doses = doses,
times = c(1, 5, 2),
prob = c(.25, .75),
`b1[1]` = 1:10 / 100,
`b1[2]` = 2:11 / 100,
`b1[3]` = 3:12 / 100,
a = 10:1 / 100,
c1 = seq(.1, 3, length = 10) / 100,
c2 = seq(- .1, - .02, length = 10) / 100,
d1 = seq(3, 4, length = 10) / 100,
d2 = seq(4, 5, length = 10) / 100,
gam = seq(.2, .33, length = 10) / 100,
true_responses = rlang::expr(
ilogit(
a + matrix(
c(`b1[1]`, `b1[2]`, `b1[3]`),
ncol = 3
)[, which(dose == !!doses)] * (
(1 - exp(- c1 * time)) / (1 - exp(- c1 * d1)) * (time < d1) +
(1 - gam * (1 - exp(- c2 * (time - d1))) /
(1 - exp(- c2 * (d2 - d1)))) * (d1 <= time & time <= d2) +
(1 - gam) * (time > d2)
)
)
)
)
test_posterior(
mcmc,
doses = doses,
times = c(1, 5, 2),
reference_dose = 3,
prob = c(.25, .75),
`b1[1]` = 1:10 / 100,
`b1[2]` = 2:11 / 100,
`b1[3]` = 3:12 / 100,
a = 10:1 / 100,
c1 = seq(.1, 3, length = 10) / 100,
c2 = seq(- .1, - .02, length = 10) / 100,
d1 = seq(3, 4, length = 10) / 100,
d2 = seq(4, 5, length = 10) / 100,
gam = seq(.2, .33, length = 10) / 100,
true_responses = rlang::expr(
ilogit(
a + matrix(
c(`b1[1]`, `b1[2]`, `b1[3]`),
ncol = 3
)[, which(dose == !!doses)] * (
(1 - exp(- c1 * time)) / (1 - exp(- c1 * d1)) * (time < d1) +
(1 - gam * (1 - exp(- c2 * (time - d1))) /
(1 - exp(- c2 * (d2 - d1)))) * (d1 <= time & time <= d2) +
(1 - gam) * (time > d2)
)
) -
ilogit(
a + matrix(
c(`b1[1]`, `b1[2]`, `b1[3]`),
ncol = 3
)[, which(reference_dose == !!doses)] * (
(1 - exp(- c1 * time)) / (1 - exp(- c1 * d1)) * (time < d1) +
(1 - gam * (1 - exp(- c2 * (time - d1))) /
(1 - exp(- c2 * (d2 - d1)))) * (d1 <= time & time <= d2) +
(1 - gam) * (time > d2)
)
)
)
)
})
test_that("MCMC: independent binary probit long IDP", {
n_chains <- 2
t_max <- 4
times <- c(0, 2, 4)
link <- "probit"
data <- dreamer_data_independent_binary(
n_cohorts = c(10, 20, 30),
dose = c(1, 3, 5),
b1 = 1:3,
link = link,
longitudinal = "linear",
a = .5,
times = times,
t_max = t_max
)
mcmc <- dreamer_mcmc(
data,
mod = model_independent_binary(
mu_b1 = 0,
sigma_b1 = 1,
link = link,
longitudinal = model_longitudinal_idp(0, 1, t_max = t_max)
),
n_iter = 5,
silent = TRUE,
convergence_warn = FALSE,
n_chains = n_chains
)
doses <- attr(mcmc, "doses")
assert_mcmc_format(mcmc, n_chains, times)
test_posterior(
mcmc,
doses = doses,
times = c(1, 5, 2),
prob = c(.25, .75),
`b1[1]` = 1:10 / 100,
`b1[2]` = 2:11 / 100,
`b1[3]` = 3:12 / 100,
a = 10:1 / 100,
c1 = seq(.1, 3, length = 10) / 100,
c2 = seq(- .1, - .02, length = 10) / 100,
d1 = seq(3, 4, length = 10) / 100,
d2 = seq(4, 5, length = 10) / 100,
gam = seq(.2, .33, length = 10) / 100,
true_responses = rlang::expr(
iprobit(
a + matrix(
c(`b1[1]`, `b1[2]`, `b1[3]`),
ncol = 3
)[, which(dose == !!doses)] * (
(1 - exp(- c1 * time)) / (1 - exp(- c1 * d1)) * (time < d1) +
(1 - gam * (1 - exp(- c2 * (time - d1))) /
(1 - exp(- c2 * (d2 - d1)))) * (d1 <= time & time <= d2) +
(1 - gam) * (time > d2)
)
)
)
)
test_posterior(
mcmc,
doses = doses,
times = c(1, 5, 2),
reference_dose = 3,
prob = c(.25, .75),
`b1[1]` = 1:10 / 100,
`b1[2]` = 2:11 / 100,
`b1[3]` = 3:12 / 100,
a = 10:1 / 100,
c1 = seq(.1, 3, length = 10) / 100,
c2 = seq(- .1, - .02, length = 10) / 100,
d1 = seq(3, 4, length = 10) / 100,
d2 = seq(4, 5, length = 10) / 100,
gam = seq(.2, .33, length = 10) / 100,
true_responses = rlang::expr(
iprobit(
a + matrix(
c(`b1[1]`, `b1[2]`, `b1[3]`),
ncol = 3
)[, which(dose == !!doses)] * (
(1 - exp(- c1 * time)) / (1 - exp(- c1 * d1)) * (time < d1) +
(1 - gam * (1 - exp(- c2 * (time - d1))) /
(1 - exp(- c2 * (d2 - d1)))) * (d1 <= time & time <= d2) +
(1 - gam) * (time > d2)
)
) -
iprobit(
a + matrix(
c(`b1[1]`, `b1[2]`, `b1[3]`),
ncol = 3
)[, which(reference_dose == !!doses)] * (
(1 - exp(- c1 * time)) / (1 - exp(- c1 * d1)) * (time < d1) +
(1 - gam * (1 - exp(- c2 * (time - d1))) /
(1 - exp(- c2 * (d2 - d1)))) * (d1 <= time & time <= d2) +
(1 - gam) * (time > d2)
)
)
)
)
})
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