Nothing
test_that("get_dose() beta binary logit", {
link <- "logit"
data <- dreamer_data_linear_binary(
n_cohorts = c(10, 20, 30),
dose = c(1, 3, 5),
b1 = 1,
b2 = 2,
link = link
)
mcmc <- dreamer_mcmc(
data,
mod = model_beta_binary(
mu_b1 = 0,
sigma_b1 = 1,
mu_b2 = 0,
sigma_b2 = 1,
mu_b3 = 0,
sigma_b3 = 1,
mu_b4 = 0,
sigma_b4 = 1,
link = link
),
n_iter = 2,
n_chains = 1,
silent = TRUE,
convergence_warn = FALSE
)
lower <- min(attr(mcmc, "doses"))
upper <- max(attr(mcmc, "doses"))
b1 <- 1:2
b2 <- c(- 1, 1)
b3 <- c(2.2, 2)
b4 <- c(.99, 1.01)
mcmc <- mcmc %>%
replace_mcmc("mod", "b1", b1) %>%
replace_mcmc("mod", "b2", b2) %>%
replace_mcmc("mod", "b3", b3) %>%
replace_mcmc("mod", "b4", b4)
scale <- attr(mcmc$mod, "scale")
dose <- 2
get_dose(
mcmc$mod,
time = NULL,
response = ilogit(
(b1 + b2 * ((b3 + b4) ^ (b3 + b4)) / (b3 ^ b3 * b4 ^ b4) *
(dose / scale) ^ b3 * (1 - dose / scale) ^ b4)
),
lower = lower,
upper = upper
) %>%
expect_equal(rep(dose, 2))
})
test_that("get_dose() beta binary probit", {
link <- "probit"
data <- dreamer_data_linear_binary(
n_cohorts = c(10, 20, 30),
dose = c(1, 3, 5),
b1 = 1,
b2 = 2,
link = link
)
mcmc <- dreamer_mcmc(
data,
mod = model_beta_binary(
mu_b1 = 0,
sigma_b1 = 1,
mu_b2 = 0,
sigma_b2 = 1,
mu_b3 = 0,
sigma_b3 = 1,
mu_b4 = 0,
sigma_b4 = 1,
link = link
),
n_iter = 2,
n_chains = 1,
silent = TRUE,
convergence_warn = FALSE
)
lower <- min(attr(mcmc, "doses"))
upper <- max(attr(mcmc, "doses"))
b1 <- 1:2
b2 <- c(- 1, 1)
b3 <- c(2.2, 2)
b4 <- c(.99, 1.01)
mcmc <- mcmc %>%
replace_mcmc("mod", "b1", b1) %>%
replace_mcmc("mod", "b2", b2) %>%
replace_mcmc("mod", "b3", b3) %>%
replace_mcmc("mod", "b4", b4)
scale <- attr(mcmc$mod, "scale")
dose <- 2
get_dose(
mcmc$mod,
time = NULL,
response = iprobit(
(b1 + b2 * ((b3 + b4) ^ (b3 + b4)) / (b3 ^ b3 * b4 ^ b4) *
(dose / scale) ^ b3 * (1 - dose / scale) ^ b4)
),
lower = lower,
upper = upper
) %>%
expect_equal(rep(dose, 2))
})
test_that("get_dose() beta binary logit longitudinal", {
link <- "logit"
times <- c(0, 10)
t_max <- max(times)
data <- dreamer_data_linear_binary(
n_cohorts = c(10, 25, 30),
dose = c(0, 2, 4),
b1 = .5,
b2 = 3,
link = link,
longitudinal = "linear",
a = .5,
times = times,
t_max = t_max
)
mcmc <- dreamer_mcmc(
data = data,
n_iter = 2,
n_chains = 1,
convergence_warn = FALSE,
silent = TRUE,
mod = model_beta_binary(
mu_b1 = 0,
sigma_b1 = 1,
mu_b2 = 0,
sigma_b2 = 1,
mu_b3 = 0,
sigma_b3 = 1,
mu_b4 = 0,
sigma_b4 = 1,
link = link,
longitudinal = model_longitudinal_linear(0, 1, t_max)
)
)
lower <- min(attr(mcmc, "doses"))
upper <- max(attr(mcmc, "doses"))
a <- c(.1, .2)
b1 <- 1:2
b2 <- c(- 1, 1)
b3 <- c(1.98, 2)
b4 <- c(.99, 1.01)
mcmc <- mcmc %>%
replace_mcmc("mod", "a", a) %>%
replace_mcmc("mod", "b1", b1) %>%
replace_mcmc("mod", "b2", b2) %>%
replace_mcmc("mod", "b3", b3) %>%
replace_mcmc("mod", "b4", b4)
time <- 3
dose <- 2
scale <- attr(mcmc$mod, "scale")
get_dose(
mcmc$mod,
time = time,
response = ilogit(
a + time / t_max *
((b1 + b2 * ((b3 + b4) ^ (b3 + b4)) / (b3 ^ b3 * b4 ^ b4) *
(dose / scale) ^ b3 * (1 - dose / scale) ^ b4))
),
lower = lower,
upper = upper
) %>%
expect_equal(rep(dose, 2))
})
test_that("get_dose() beta binary probit longitudinal", {
link <- "probit"
times <- c(0, 10)
t_max <- max(times)
data <- dreamer_data_linear_binary(
n_cohorts = c(10, 25, 30),
dose = c(0, 2, 4),
b1 = .5,
b2 = 3,
link = link,
longitudinal = "linear",
a = .5,
times = times,
t_max = t_max
)
mcmc <- dreamer_mcmc(
data = data,
n_iter = 2,
n_chains = 1,
convergence_warn = FALSE,
silent = TRUE,
mod = model_beta_binary(
mu_b1 = 0,
sigma_b1 = 1,
mu_b2 = 0,
sigma_b2 = 1,
mu_b3 = 0,
sigma_b3 = 1,
mu_b4 = 0,
sigma_b4 = 1,
link = link,
longitudinal = model_longitudinal_linear(0, 1, t_max)
)
)
lower <- min(attr(mcmc, "doses"))
upper <- max(attr(mcmc, "doses"))
a <- c(.1, .2)
b1 <- 1:2
b2 <- c(- 1, 1)
b3 <- c(1.98, 2)
b4 <- c(.99, 1.01)
mcmc <- mcmc %>%
replace_mcmc("mod", "a", a) %>%
replace_mcmc("mod", "b1", b1) %>%
replace_mcmc("mod", "b2", b2) %>%
replace_mcmc("mod", "b3", b3) %>%
replace_mcmc("mod", "b4", b4)
time <- 3
dose <- 2
scale <- attr(mcmc$mod, "scale")
get_dose(
mcmc$mod,
time = time,
response = iprobit(
a + time / t_max *
((b1 + b2 * ((b3 + b4) ^ (b3 + b4)) / (b3 ^ b3 * b4 ^ b4) *
(dose / scale) ^ b3 * (1 - dose / scale) ^ b4))
),
lower = lower,
upper = upper
) %>%
expect_equal(rep(dose, 2))
})
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